Fact-checked by Grok 2 weeks ago

Functional imaging

Functional imaging refers to a class of techniques that detect and measure physiological changes associated with tissue activity, particularly in the , by tracking variations in blood flow, metabolism, oxygenation, or neuronal electrical activity during rest or task performance. These methods provide non-invasive, insights into function, enabling the mapping of cognitive, sensory, and motor processes at a regional level. Key techniques in functional imaging include functional magnetic resonance imaging (fMRI), which uses blood-oxygen-level-dependent (BOLD) contrast to capture hemodynamic responses with high (approximately 2-3 mm) but moderate temporal resolution (seconds); positron emission tomography (PET), which quantifies regional cerebral blood flow or glucose metabolism using radioactive tracers, offering good (4-5 mm) but involving ; and electroencephalography (EEG), which records electrical fields from neuronal activity with excellent temporal resolution (milliseconds) but lower spatial accuracy (~1 cm), or magnetoencephalography (MEG), with spatial accuracy of 2-5 mm for superficial sources. Other modalities, such as near-infrared spectroscopy (NIRS) for superficial cortical monitoring and single-photon emission computed tomography (SPECT) for receptor binding and blood flow assessment, extend the toolkit for specific applications. Functional imaging has revolutionized and clinical practice by facilitating the study of plasticity after injury, the of neurological disorders like , research into psychiatric conditions like , and the evaluation of treatment outcomes in . For instance, it reveals task-specific activations and functional networks, such as the , which are disrupted in conditions like or . Despite limitations like motion artifacts in fMRI or radiation exposure in , ongoing advancements in and higher-resolution imaging continue to enhance its precision and accessibility.

Introduction

Definition and Scope

Functional imaging refers to a collection of noninvasive techniques designed to map and measure dynamic physiological processes, such as neural activity, , or blood flow, within organs like the , in contrast to static anatomical features. These methods enable the of functional changes over time, distinguishing them from structural , which primarily delineates anatomical structures such as or size without capturing activity patterns. For instance, while structural might identify the location and extent of a tumor based on its physical boundaries, functional imaging reveals how surrounding tissues respond during cognitive tasks or at rest. The scope of functional imaging is broad but centers on fields like and , where it assesses task-evoked responses—such as activation during —or spontaneous resting-state fluctuations to infer underlying physiological states. In , it predominantly targets function to study and , whereas in , it evaluates cardiac motion and dynamics across the heartbeat cycle. These applications rely on noninvasive modalities that detect indirect proxies of function, allowing repeated measurements in clinical and research settings without invasive procedures. Central to functional imaging are concepts like the hemodynamic response, which describes the neurovascular changes that link neural events to detectable blood flow alterations; neural coupling, the coordinated relationship between neuronal firing and associated hemodynamic signals; and functional connectivity, the temporal correlations in activity between anatomically distinct regions that reveal integrated network dynamics. These themes underpin the interpretation of data across techniques, emphasizing how functional imaging elucidates not just isolated activations but the spatiotemporal orchestration of biological processes.

Historical Development

The conceptual foundations of functional emerged in the late , when Charles S. Roy and Charles Sherrington conducted pioneering animal experiments demonstrating that local neural activity is coupled to changes in cerebral blood flow. Their 1890 study on cats and monkeys showed that stimulating sensory nerves increased blood supply to corresponding regions, laying the groundwork for later hemodynamic-based techniques. In the mid-20th century, quantitative assessments of cerebral blood flow advanced through animal studies, such as those using the nitrous oxide method adapted for rodents and the development of autoradiographic techniques in the and , which enabled precise mapping of regional changes in response to stimuli. These efforts, building on human applications by Seymour Kety and Carl F. Schmidt in the 1940s, established the physiological basis for linking and function non-invasively. The 1970s and 1980s marked the emergence of () as the first practical functional imaging modality. Precursors included the 1951 positron brain scanner prototype developed by physicist Gordon L. Brownell and neurosurgeon William H. Sweet at , which used opposing sodium iodide crystals to detect annihilation photons for localized tumor imaging. The first human PET scans occurred in 1976 at , where Michael E. Phelps and colleagues employed 18F-fluorodeoxyglucose (FDG) to measure cerebral glucose metabolism, enabling quantitative visualization of brain activity . This breakthrough, formalized in subsequent publications, shifted focus from static to dynamic function and spurred radiotracer developments for broader applications. The 1990s brought a revolutionary advance with the invention of (fMRI), particularly through blood-oxygen-level-dependent (BOLD) . In 1990, Seiji Ogawa and colleagues at Bell Laboratories demonstrated in rat brain studies that deoxyhemoglobin acts as an endogenous , with MRI signal changes reflecting oxygenation variations during . Building on this, Kenneth K. Kwong's team at achieved the first human BOLD fMRI experiment in May 1991, capturing activation without exogenous . The technique gained rapid adoption following presentations at the 10th Annual Meeting of the Society for Magnetic Resonance in Medicine in August 1991, where multiple groups shared confirmatory results, integrating fMRI with to map mental processes like attention and memory. From the 2000s onward, functional imaging evolved toward portability, accessibility, and integration. Advances in (EEG) and (MEG) included wireless, high-density systems in the early 2000s, enabling ambulatory recordings outside labs and improving for dynamics. (NIRS) matured for bedside use during this period, with portable devices allowing non-invasive hemodynamic monitoring in clinical settings like neonatal intensive care and intraoperative environments. Multimodal integration accelerated, exemplified by simultaneous EEG-fMRI protocols developed in the late 1990s and refined in the 2000s to combine spatial and temporal data for enhanced connectivity analysis. Post-2010, resting-state fMRI surged in prominence through initiatives like the , revealing intrinsic networks without tasks and transforming studies of disorders like . In the 2020s, functional imaging has continued to advance with the incorporation of and for improved data analysis and interpretation, particularly in fMRI preprocessing and connectivity mapping. Multimodal approaches, such as combined fMRI and (fNIRS), have gained traction for more comprehensive function assessment in both and clinical settings. Emerging techniques, including functional conductivity imaging introduced in 2024, offer new ways to measure neural activity directly. As of 2025, the has driven further innovations in high-resolution imaging and large-scale datasets, enhancing understanding of dynamics and supporting applications in and .

Principles

Physiological Basis

Functional imaging relies on the physiological principle of , where transient neural activity triggers an increase in local metabolic demand, prompting a rise in cerebral blood flow (CBF) and associated changes in blood oxygenation to meet this demand. This involves coordinated signaling from neurons and to vascular cells, such as and cells, which dilate arterioles to enhance oxygen and nutrient delivery while removing metabolic byproducts. The process ensures that active brain regions receive disproportionate hemodynamic support compared to baseline states, forming the foundational mechanism exploited by modalities like fMRI and . The hemodynamic response function (HRF) characterizes the temporal dynamics of this neurovascular response, manifesting as a delayed increase in blood flow (typically 2-6 seconds after neural onset) that peaks around 4-6 seconds and outlasts the initiating activity by 10-20 seconds, followed by an undershoot phase. This lag arises from the time required for vasodilatory signals to propagate and for vascular compliance to adjust. Qualitatively, the HRF is often modeled as a "" effect, where increased CBF inflates and venous volumes, reducing deoxyhemoglobin concentration and thereby enhancing tissue oxygenation beyond the proportional metabolic need. Metabolic markers further underpin functional imaging by serving as proxies for neural activity. Increases in cerebral metabolic rate of glucose (CMRglc) and oxygen (CMRO2) during reflect heightened demands, though CBF rises more steeply (by 50-100%) than CMRO2 (20-40%), leading to hyperoxygenation. Active tissue also produces via in and neurons, which acts as an shuttle to sustain prolonged without immediate oxidative . These shifts in glucose, oxygen, and dynamics provide indirect measures of function across techniques. In resting states, intrinsic fluctuations in blood oxygenation level-dependent (BOLD)-like signals occur spontaneously, reflecting baseline neural connectivity and ongoing network interactions without external tasks. These low-frequency oscillations (0.01-0.1 Hz) arise from coordinated neuronal ensemble activity and hemodynamic variability, enabling the mapping of functional networks such as the . While the brain emphasizes neuronal firing and synaptic processes in its physiological adaptations, functional imaging in other organs like the heart focuses on perfusion mismatches, where discrepancies between blood supply and metabolic demand—such as reduced flow in ischemic regions despite preserved viability—reveal dysfunction. In cardiac applications, activation (e.g., via stress) highlights areas of inadequate perfusion relative to oxygen utilization, aiding viability assessment.

Signal Detection and Measurement

Functional imaging techniques primarily rely on indirect measurements of neural activity, capturing secondary physiological responses such as changes in blood flow, oxygenation, or rather than the neural electrical signals themselves. For instance, hemodynamic methods detect variations in cerebral blood flow (CBF) and blood oxygenation as proxies for neuronal , driven by the between increased metabolic demand and vascular responses. In contrast, direct methods measure the electromagnetic fields generated by neuronal currents, providing a more immediate readout of activity but often at the cost of lower spatial . These approaches bridge the gap between physiological events and detectable signals, enabling non-invasive assessment of function. A fundamental trade-off in functional imaging exists between spatial and temporal resolution, dictated by the underlying physical principles of signal propagation and detection. Hemodynamic-based techniques offer millimeter-scale spatial resolution (typically 1-3 mm) but are limited to seconds-long temporal resolution due to the slow nature of vascular responses. Electrophysiological methods, conversely, achieve millisecond temporal resolution to capture rapid neural dynamics but suffer from poor spatial localization, often on the order of centimeters, owing to volume conduction effects in tissue. This dichotomy arises because indirect signals integrate over larger tissue volumes and slower processes, while direct signals reflect localized, fast transients but diffuse broadly. Noise in functional imaging arises from multiple sources, including physiological artifacts such as motion, cardiac pulsations, and , as well as instrumental factors like scanner thermal and magnetic field instabilities. These contaminants can obscure subtle functional signals, necessitating strategies to enhance the (SNR), which is typically around 100:1 in standard acquisitions. Optimization often involves signal averaging across multiple trials or epochs to improve SNR, as the functional signal coheres while random diminishes. Quantitative assessment of functional signals focuses on metrics like percent signal change, which quantifies modulation relative to baseline; for example, hemodynamic responses exhibit 1-5% changes in blood oxygenation level-dependent (BOLD) signals during task activation. Calibration techniques, such as hypercapnia-induced , enable absolute quantification of CBF, converting relative changes into physiological units like ml/100g/min to better interpret neural-metabolic coupling. Safety in functional imaging must account for exposure risks inherent to detection methods. Nuclear imaging modalities involve , with typical effective doses of approximately 5-10 mSv per scan, optimized under the ALARA principle to minimize risks like , in accordance with guidelines from regulatory bodies such as the ICRP. Magnetic-based techniques pose contraindications for individuals with ferromagnetic implants or pacemakers due to , heating, and projectile effects from static fields exceeding 1.5-7 T. Screening protocols ensure these risks are mitigated through patient history and device compatibility assessments.

Modalities

Functional Magnetic Resonance Imaging (fMRI)

(fMRI) is a non-invasive technique for mapping activity by detecting variations in oxygenation associated with neural processes. It predominantly utilizes blood-oxygenation-level-dependent (BOLD) , which exploits the paramagnetic effect of deoxyhemoglobin on the , leading to alterations in the T2* relaxation time of nearby water protons. Upon increased neuronal firing, local cerebral flow exceeds oxygen consumption, reducing deoxyhemoglobin concentration and thereby prolonging T2*, which enhances the MRI signal in activated regions. This indirect measure of neural activity forms the core of BOLD fMRI, providing a surrogate for hemodynamic responses coupled to . The BOLD signal change is quantitatively approximated by the relation \frac{\Delta S}{S} \approx -TE \cdot \Delta R_2^* where TE denotes the echo time and \Delta R_2^* represents the change in the effective transverse relaxation rate driven by oxygenation shifts. In practice, fMRI acquisitions employ echo-planar imaging (EPI) sequences for efficient whole-brain coverage, typically at 3 T with parameters such as a time (TR) of 2 s, TE of 30 ms, and isotropic dimensions of 2-3 mm. Experimental designs include task-based paradigms—such as block or event-related formats to elicit targeted responses—or resting-state protocols to examine spontaneous fluctuations in . Key advantages of fMRI include its ability to achieve whole-brain imaging without , facilitating serial studies in healthy and clinical populations. It delivers of approximately 1-3 mm and an effective of 1-2 s, surpassing (PET) in spatial detail and repeatability due to endogenous contrast, whereas PET offers metabolic specificity via tracers but at lower resolution and with . In contrast to (EEG) or (MEG), which provide millisecond temporal precision through direct neural signals, fMRI excels in anatomical localization despite its slower sampling. fMRI data are prone to specific artifacts, including geometric distortions from susceptibility-induced field inhomogeneities, especially in ventral brain areas, necessitating techniques like fieldmap-based corrections. Motion artifacts demand prospective prevention or retrospective realignment, while physiological noise from cardiac pulsations and respiration can mimic or obscure BOLD signals, often addressed through regression models incorporating peripheral measurements. Notable variants extend fMRI's utility: arterial spin labeling (ASL) magnetically tags inflowing blood to enable quantitative cerebral blood flow (CBF) mapping without exogenous agents, though with reduced signal-to-noise compared to BOLD. Diffusion-weighted sequences can further assess white matter tract integrity for structural connectivity analyses, complementing BOLD-derived functional networks.

Positron Emission Tomography (PET)

Positron emission tomography () is a imaging technique that provides quantitative assessment of metabolic processes, blood flow, and receptor binding by detecting gamma rays emitted indirectly from positron-emitting radiotracers. These tracers, analogs of biologically active molecules labeled with short-lived radioisotopes such as (18F), are injected into the patient and accumulate in tissues based on physiological activity, enabling the visualization of functional changes at the molecular level. Unlike anatomical imaging modalities, PET offers absolute quantification of tracer uptake, making it particularly valuable for , , and applications where metabolic alterations are key diagnostic indicators. The core mechanism of PET relies on the decay of positron-emitting radionuclides within the tracer, which travel a short (typically 0.5-2 ) before annihilating with an in surrounding , producing two 511 keV photons emitted at approximately 180 degrees apart. A ring of detectors surrounding the patient captures these photons—pairs detected within a narrow time window (e.g., 6-12 nanoseconds)—to localize the annihilation event along the line of response, rejecting scattered or random events for improved image quality. algorithms, such as filtered back-projection or iterative methods, then generate three-dimensional images of radiotracer distribution from these projections. A common tracer, 18F-fluorodeoxyglucose (18F-FDG), mimics glucose and highlights regions of elevated , such as in hypermetabolic tumors. Quantitative analysis uses the standardized uptake value (), defined as the ratio of radioactivity concentration (in /mL or μCi/mL) to the injected dose per unit body weight (in /kg or μCi/kg), providing a normalized measure of uptake independent of administered dose. In a typical PET procedure, short-lived tracers are produced on-site via bombardment, such as the 18O(p,n)18F reaction for , which has a physical of 109.8 minutes. Following intravenous injection, patients undergo an uptake period of 10-60 minutes to allow tracer distribution and accumulation, after which occurs over 10-30 minutes per bed position in a whole-body scan. Scans can be static, capturing a snapshot of uptake, or dynamic, acquiring serial images to model tracer and compartmental processes like transport and . of photons by body tissues is corrected using integrated computed (CT) for both attenuation maps and anatomical co-registration, ensuring accurate quantification. PET excels in providing absolute quantification of physiological parameters, such as cerebral glucose with 18F-FDG, myocardial blood flow with 13N-ammonia, or receptor with ligands like 11C-raclopride for imaging. It achieves of approximately 4-6 mm, sufficient for detecting lesions down to 5-10 mm, and on the order of minutes, allowing observation of dynamic processes over scan durations. These capabilities support precise monitoring of treatment response, where changes in can indicate therapeutic efficacy. Despite its strengths, PET involves ionizing radiation exposure, with an effective dose of about 5-10 mSv from the PET component alone (e.g., 7-8 mSv for 18F-FDG), comparable to several years of natural background radiation but cumulative in repeated scans. The short half-lives of tracers necessitate on-site cyclotron facilities, driving high operational costs estimated at $2,000–$6,000 per scan in the US (as of 2025), and limiting availability to specialized centers. Additionally, partial volume effects degrade quantification in small structures due to the finite resolution. Hybrid systems like integrate PET's functional data with MRI's superior soft-tissue contrast and eliminate separate CT radiation, enabling simultaneous acquisition for applications in and with reduced overall dose.

(SPECT)

(SPECT) is a imaging technique that visualizes and quantifies physiological processes, such as regional cerebral blood flow (rCBF) and receptor binding, by detecting single gamma photons emitted from radioisotope-labeled tracers. Unlike PET, which uses positron annihilation for coincidence detection, SPECT employs gamma cameras that rotate around the patient to acquire projections, offering a more accessible alternative without the need for on-site cyclotrons due to longer-lived isotopes like (, half-life 6 hours). Common brain perfusion tracers include -hexamethylpropyleneamine (HMPAO) or ethyl cysteinate dimer (ECD), which cross the blood-brain barrier and trap in proportion to rCBF, enabling functional assessment of conditions like , , and . In SPECT imaging, the , equipped with collimators to define photon direction, collects data from multiple angles, followed by using filtered back-projection or iterative algorithms to produce images. Procedures typically involve intravenous tracer injection, a 10-30 minute uptake period, and 20-40 minutes of acquisition, often combined with for correction and anatomical in hybrid SPECT/ systems. While SPECT provides relative quantification of tracer distribution rather than absolute values like PET, it supports semi-quantitative analysis through ratios of regional to reference uptake. SPECT achieves of 8-12 mm, coarser than PET due to collimator limitations and photon scatter, with limited to minutes for static imaging. Its advantages include lower cost (typically $800–$2,000 per scan), widespread availability using commercial generators for 99mTc, and reduced radiation dose (about 10-20 mSv for brain perfusion studies), making it suitable for routine clinical use in . Applications in functional brain imaging include evaluating hypoperfusion in or hyperperfusion in foci, guiding diagnosis and treatment. Limitations encompass lower compared to for , as well as artifacts from patient motion or soft-tissue , though systems mitigate these. SPECT remains valuable for bedside or resource-limited settings, complementing other modalities in approaches.

Electroencephalography (EEG) and Magnetoencephalography (MEG)

(EEG) measures the electrical activity of the brain by recording voltage fluctuations on the resulting from the summation of excitatory and inhibitory postsynaptic potentials in large populations of cortical neurons. These potentials arise primarily from synchronized synaptic currents in pyramidal cells oriented to the cortical surface, generating detectable extracellular fields that propagate through the and . Standard EEG recordings employ the 10-20 electrode placement system, developed by Herbert Jasper in 1958, which positions electrodes at 10% or 20% intervals along the to ensure proportional coverage across head sizes and shapes. Signals are typically bandpass filtered between 0.5 and 100 Hz to isolate relevant neural oscillations while attenuating low-frequency drifts and high-frequency artifacts. Source estimation in EEG involves solving the ill-posed , often using techniques like fitting to model intracranial current sources from scalp potentials, though this requires assumptions about head and source configuration. Magnetoencephalography (MEG) complements EEG by detecting the weak magnetic fields produced by the same intracellular neural currents, using superconducting quantum interference devices (SQUIDs) cooled to near-absolute zero for high sensitivity. Unlike electrical potentials in EEG, magnetic fields from tangential currents pass through the head tissues without significant distortion from the or , providing cleaner localization of superficial cortical sources. Modern MEG systems utilize whole-head arrays, typically comprising 200-300 channels, to capture activity across the entire simultaneously, enabling comprehensive mapping of brain dynamics. Both EEG and MEG procedures are non-invasive, involving the placement of caps or helmets on the subject's head, often aligned with anatomical landmarks for accurate co-registration with structural . Recordings can elicit event-related potentials (ERPs) or fields by averaging responses to repeated stimuli, revealing evoked neural activity with millisecond precision. These modalities offer exceptional below 1 ms, allowing real-time tracking of neural processes, while source-localized reaches approximately 5-10 mm for cortical generators, depending on head modeling and noise levels. Key advantages of EEG and MEG include their ability to monitor brain oscillations in real time, such as alpha waves in the 8-12 Hz range associated with relaxed and prominent over posterior regions. EEG, in particular, is portable, low-cost, and suitable for or long-term monitoring outside shielded environments, facilitating studies of dynamic cognitive and sensory processes. Despite these strengths, both techniques exhibit poor to deep sources, such as those in subcortical structures, due to rapid signal with distance from the sensors. EEG is particularly susceptible to volume conduction effects, where currents spread isotropically through tissues, blurring source localization and introducing spurious correlations between distant electrodes. MEG, while less affected by conduction, is vulnerable to biomagnetic noise from environmental or physiological sources, such as cardiac or muscle activity, which can degrade signal-to-noise ratios in unshielded settings.

Optical and Ultrasound Techniques

Optical and ultrasound techniques represent portable and non-invasive modalities for functional imaging, primarily targeting hemodynamic changes in superficial regions. These methods leverage or waves to monitor cerebral blood flow and oxygenation, offering alternatives to more stationary techniques like fMRI or for real-time, bedside applications. (NIRS), a key optical approach, measures functional activity by detecting diffuse absorption changes due to concentrations in cortical . It operates on the modified , which accounts for scattering in : \Delta[\mathrm{Hb}] = \frac{\Delta A}{\epsilon \cdot d \cdot \mathrm{DPF}} where \Delta[\mathrm{Hb}] is the change in hemoglobin concentration, \Delta A is the change in absorbance, \epsilon is the , d is the source-detector , and DPF is the differential pathlength factor correcting for path elongation. NIRS typically uses wavelengths between 700 and 900 nm, enabling penetration of approximately 2-3 cm into the , sufficient for sampling superficial layers like the prefrontal and motor areas. In practice, NIRS involves wearable optodes placed on the to emit and detect near-infrared , often in configurations for during tasks such as cognitive assessments or motor activities. This setup allows continuous recording with a of about 1 second and around 1 cm, making it suitable for portable studies in naturalistic settings. Advantages include its non-ionizing nature, low cost, and ease of use at the bedside without requiring a controlled environment, facilitating applications in , , and . However, limitations arise from its shallow penetration depth, restricting imaging to cortical regions only, and susceptibility to motion artifacts from head movements or superficial blood flow, which can confound signals. Functional (fUS), an emerging -based technique, images function by quantifying cerebral (CBF) changes via Doppler effects, serving as a for neural activity through neurovascular . It employs power Doppler or microbubble contrast agents to detect motion, achieving high frame rates up to 100 Hz for dynamic imaging of vascular responses in the . in fUS exceeds 100 μm, allowing fine-scale mapping in small-animal models or human superficial structures. Procedures for fUS utilize transcranial probes applied to the or open in surgical contexts, with recent prototypes enabling use similar to NIRS setups. This portability supports monitoring during behaviors or interventions, with no and compatibility with other modalities. Key benefits include its superior spatiotemporal resolution compared to optical methods and deep penetration through acoustic windows, though limited to regions accessible via (e.g., avoiding dense ). Unique challenges involve the need for a clear acoustic window, which can be obstructed by the in some individuals, and potential sensitivity to motion or bubble stability in contrast-enhanced modes.

Data Analysis

Preprocessing Methods

Preprocessing methods in functional imaging involve a series of computational steps to clean and standardize raw data, mitigating artifacts and ensuring comparability across subjects and sessions. These procedures address issues such as head motion, physiological fluctuations, and acquisition-related distortions, which can otherwise confound signal interpretation. Common pipelines, implemented in software like SPM, FSL, and AFNI, typically proceed sequentially from motion correction to normalization and smoothing, with modality-specific adjustments applied as needed. Recent advancements incorporate artificial intelligence (AI) and machine learning (ML) for automated preprocessing, such as deep learning-based motion correction and physiological noise removal, improving efficiency and accuracy in large-scale datasets as of 2025. Artifact correction begins with motion realignment, which estimates and compensates for rigid-body movements using six parameters: three translations and three rotations. This process aligns all volumes in a to a reference image, often the mean or first volume, via least-squares optimization to minimize spatial discrepancies. Rigid-body transformations preserve anatomy without deformation, effectively reducing motion-induced signal variance in fMRI . Physiological noise regression further removes cyclic artifacts from cardiac and respiratory sources, which can contribute up to 50% of low-frequency signal variance in fMRI. The RETROICOR method, a seminal retrospective image-based correction, models these effects using expansions of recorded physiological waveforms, regressing out phase-locked components from the BOLD signal. Spatial normalization registers individual images to a standard template, such as the MNI152 space, to enable group-level analysis. This typically combines an initial for global scaling, rotation, and translation with nonlinear warps to account for anatomical variability, achieving sub-millimeter alignment accuracy in high-resolution data. Slice-timing correction complements this by interpolating to a common acquisition time, correcting for delays in interleaved slice orders that can bias hemodynamic response estimates by up to 20% in event-related designs. Smoothing applies a low-pass spatial filter, commonly an isotropic Gaussian kernel with 6 mm full-width at half-maximum (FWHM), to enhance (SNR) and compensate for the inherent spatial blur of the hemodynamic response , which approximates 4-6 mm. This step increases statistical power for detecting activations but risks blurring fine-scale features, with optimal kernel size balancing noise reduction against over-smoothing. Modality-specific preprocessing addresses unique acquisition challenges. In fMRI, fieldmap unwarping uses phase-difference images from dual-echo gradient-echo sequences to estimate and correct B0 inhomogeneity-induced distortions, which can displace voxels by several millimeters in frontal and temporal regions. For PET, attenuation correction scales emission data by mu-maps derived from transmission scans or , while scatter correction employs single-scatter or subtraction to restore quantitative accuracy, reducing underestimation by approximately 20-30% in deep tissues. In EEG, (ICA) decomposes multichannel signals into spatially fixed, temporally independent components, enabling the identification and subtraction of ocular artifacts like blinks and saccades, which account for up to 10-20% of variance in frontal channels. Quality control ensures data integrity through quantitative metrics, including checks for signal variance stability and outlier volume detection via or framewise displacement thresholds exceeding 0.5 mm. These assessments flag volumes with excessive noise or motion for scrubbing, maintaining dataset reliability before downstream analysis.

Statistical and Interpretive Approaches

In functional imaging, the general (GLM) serves as a foundational framework for voxel-wise , modeling the observed signal y as y = X\beta + \epsilon, where X is the incorporating stimulus onsets convolved with the hemodynamic response function (HRF), \beta represents estimates, and \epsilon denotes . This approach enables the detection of task-related activations by estimating \beta via and assessing significance through F-tests on contrasts of interest, such as t-tests for specific conditions. The GLM's flexibility accommodates both block and event-related designs, assuming linearity and , which underpins its widespread adoption in modalities like fMRI. To address the inherent in whole-brain analyses, where thousands of voxels are tested simultaneously, corrections such as family-wise error (FWE) rates are applied using random field theory (RFT), which accounts for spatial correlations to control the probability of false positives across the image. Alternatively, (FDR) methods, like the Benjamini-Hochberg procedure, offer a less conservative approach by controlling the expected proportion of false positives among significant results, often preferred for exploratory studies. Cluster-level thresholding complements these by identifying contiguous suprathreshold voxels, enhancing sensitivity while maintaining FWE control through permutation testing or RFT-based approximations. Connectivity analysis extends beyond univariate activation mapping to reveal functional networks, employing seed-based to compute Pearson correlations between a predefined region's time course and all other voxels, thereby delineating regions coactivating with the seed. Independent components analysis (ICA) decomposes into spatially components via blind source separation, isolating intrinsic networks without prior hypotheses, as validated in resting-state fMRI studies. metrics, such as degree centrality, quantify by representing brain regions as nodes and correlations as edges, highlighting hub-like structures in functional connectomes. Emerging techniques, including models, enhance connectivity analysis by predicting whole-brain dynamics from partial or automating network detection, with applications in diagnosing neurological disorders as of 2025. Modalities-specific adaptations refine these approaches; in PET, kinetic modeling estimates tracer compartmentalization using compartmental models, such as the two-tissue model, to derive quantitative binding potentials from dynamic uptake data. For EEG, time-frequency decomposition via wavelet transforms extracts oscillatory power and phase across frequencies, enabling analysis of event-related or desynchronization in non-stationary signals. Interpretation pitfalls undermine reliability, notably circular analysis or "double-dipping," where the same dataset selects regions of interest and tests hypotheses within them, inflating effect sizes and false positives. Task-based paradigms may also lack , as controlled stimuli fail to capture real-world cognitive dynamics, necessitating cautious generalization from imaging results.

Applications

Clinical Uses

Functional imaging plays a crucial role in presurgical mapping for patients undergoing or tumor resection surgery, particularly in localizing eloquent such as and motor areas to minimize postoperative deficits. Functional MRI (fMRI) and (EEG) are commonly employed to identify these regions non-invasively, achieving 80-90% concordance with invasive methods like the for lateralization and direct cortical stimulation for motor mapping. This high agreement allows clinicians to tailor resection strategies, reducing the reliance on invasive procedures that carry a 3-5% risk of complications such as neurological morbidity. By integrating fMRI and EEG data, surgical teams can optimize outcomes, with studies demonstrating improved control and preserved function in patients. In neurological disorders, (PET) using tracers like 18F-florbetapir enables the detection of in , aiding in early diagnosis and differentiation from other dementias. This imaging modality visualizes beta-amyloid deposition with high sensitivity, supporting clinical decisions on disease progression and potential therapeutic interventions. Similarly, fMRI is utilized to monitor by assessing changes in activation patterns during , helping predict functional outcomes and guide personalized therapy. Psychiatric applications include resting-state fMRI to identify alterations in the (DMN) in patients with , including reduced connectivity. () further aids in by mapping neural activity associated with auditory hallucinations, revealing aberrant involvement that informs targeted strategies. Beyond and , functional imaging extends to other areas such as cardiac for assessing myocardial viability in ischemic heart disease, where 18F-FDG uptake distinguishes hibernating from scarred tissue to determine benefits. In , near-infrared spectroscopy (NIRS) provides bedside monitoring of cerebral oxygenation in neonates at risk of injury, detecting hypoxia-ischemia early to enable timely interventions like therapeutic . Meta-analyses of presurgical applications in confirm overall 80-90% concordance rates, underscoring the reliability of these techniques in clinical practice.

Research Applications

Functional imaging techniques have significantly advanced by enabling the mapping of brain networks underlying memory and attention processes. Task-based (fMRI) paradigms, such as the delayed match-to-sample task, consistently demonstrate robust activation in the during operations, highlighting its role in maintaining and manipulating information. These paradigms reveal how attentional demands modulate activity across fronto-parietal networks, providing insights into the neural basis of cognitive control. Complementing fMRI's spatial resolution, (MEG) excels in capturing the temporal dynamics of perceptual timing, with studies showing event-related fields peaking around 100-200 ms post-stimulus in visual cortices, underscoring the brain's millisecond-scale processing of temporal sequences. Connectivity studies using resting-state fMRI have identified intrinsic large-scale networks, including the default mode network (DMN)—active during introspection and mind-wandering—and the salience network, which detects behaviorally relevant stimuli and switches between internal and external focus. Graph-based analyses of these networks quantify topological properties like modularity and centrality, revealing hub disruptions in neurodevelopment; for instance, reduced integration in frontoparietal hubs during adolescence correlates with emerging executive functions. Such approaches have illuminated atypical connectivity patterns in developmental disorders, emphasizing the dynamic maturation of network efficiency from childhood to adulthood. In pharmacological research, (PET) receptor imaging has elucidated drug binding mechanisms, such as decreased D2 receptor availability in the of individuals with , which correlates with impaired reward processing and vulnerability to . Longitudinal fMRI studies track following pharmacological interventions, showing enhanced BOLD signals in motor and cognitive regions after treatments like antidepressants, indicative of synaptic reorganization and recovery of network function. Across species, functional ultrasound (fUS) in provides high spatiotemporal resolution for circuit-level investigations, mapping hemodynamic responses to sensory stimuli in subcortical pathways during behavioral tasks in freely moving animals. Similarly, (NIRS) in human infants detects lateralized cortical activation during native language exposure, revealing early hemispheric specialization for phonetic processing and word segmentation. Key findings from functional imaging include the identification of the human mirror neuron system through early fMRI studies, which demonstrated overlapping activations in the and during action observation and execution, supporting mechanisms of and . Large-scale consortia like the , initiated in 2010, have generated comprehensive datasets of task and resting-state fMRI, uncovering variability in functional connectivity across individuals and establishing normative maps of brain networks that inform models of and .

Limitations and Challenges

Technical Limitations

Functional imaging techniques, while powerful for mapping activity, are constrained by inherent physical and methodological limitations that impact their precision and utility. These include trade-offs in spatiotemporal , indirect measures of neural activity, modality-specific hurdles, challenges in quantitative interpretation, and demands on computational infrastructure. Such constraints necessitate careful experimental design and to mitigate artifacts and biases. A primary limitation arises from resolution trade-offs across modalities. In (fMRI), spatial resolution typically achieves 1-3 mm, enabling localization of activity to specific regions, but temporal resolution is limited by the hemodynamic response function (HRF), which smears neural events over 6-10 seconds due to delayed blood oxygenation changes. Conversely, electroencephalography (EEG) offers excellent temporal resolution below 1 ms, capturing rapid neural dynamics, yet its spatial resolution is blurred to approximately 5-9 cm by volume conduction effects through the head's tissues, complicating precise source localization. Many functional imaging methods rely on indirect inference of neural activity, introducing misalignment between observed signals and underlying neuronal events. In fMRI, the hemodynamic —where BOLD signals peak 4-6 seconds after neural onset—can distort timing estimates, particularly for fast cognitive processes, and lead to false negatives in regions with vascular impairments, such as near lesions where reduced blood flow attenuates the response. This indirect nature means functional imaging often proxies metabolic or vascular changes rather than direct electrical activity, potentially overlooking subtle or transient neural phenomena. Modality-specific technical barriers further restrict applicability. (PET) involves , with effective doses of 5-18 mSv per scan, limiting its use in pediatric populations and repeated longitudinal studies to adhere to the as low as reasonably achievable (ALARA) principle and minimize cancer risk. (MEG) requires operation in magnetically shielded rooms to exclude , as superconducting sensors are highly sensitive to external fields, increasing setup complexity and cost. (NIRS) suffers from signal attenuation by the skull, with variability in thickness reducing sensitivity by up to 80% and introducing inter-subject inconsistencies in depth penetration. Quantification poses additional challenges, particularly for BOLD-fMRI, where signals are relative—lacking absolute units—and vary in amplitude and shape across individuals due to differences in vascular and HRF , complicating comparisons and normative modeling. Inter-subject variability in HRF parameters, such as peak latency and width, can exceed 20-30% , further hindering reproducible metrics without advanced . The high dimensionality of functional imaging data exacerbates computational demands. Four-dimensional fMRI datasets, comprising thousands of voxels sampled over minutes to hours, generate terabyte-scale volumes that strain storage, processing power, and , often requiring techniques to manage noise and multiple comparisons. These resource-intensive analyses can limit applications and accessibility in resource-constrained settings.

Ethical and Practical Issues

One major practical barrier to the widespread adoption of functional techniques is their high cost, which limits accessibility, particularly in low-resource settings. For instance, a MRI commonly used for fMRI typically costs between $900,000 and over $2 million, excluding installation and maintenance expenses. Similarly, cyclotrons, essential for on-site radiotracer production, can exceed $5 million when including facility setup and operational requirements. These expenses contribute to stark disparities, as advanced imaging facilities are concentrated in high-income countries, leaving low- and middle-income regions underserved and exacerbating inequities. In low-resource environments, the scarcity of such equipment often results in delayed diagnoses and unequal treatment outcomes for neurological conditions. Privacy concerns and the need for robust informed consent processes further complicate the ethical landscape of functional imaging. Neuroimaging data can reveal incidental findings, such as asymptomatic tumors or structural anomalies, which occur in up to 10-20% of research scans and pose dilemmas regarding disclosure and follow-up care. These risks are heightened in studies involving vulnerable populations, such as children, elderly individuals, or those with cognitive impairments, where obtaining truly requires tailored safeguards to ensure comprehension and voluntariness. Ethical frameworks emphasize protecting participant while balancing potential benefits against unintended psychological or medical burdens from such discoveries. Interpretive biases in functional imaging analysis can lead to misleading conclusions with real-world implications. A common issue is the overreliance on group-averaged data, which masks substantial individual variability in brain activation patterns and may result in generalized interpretations that do not apply to specific patients. In non-clinical contexts, this extends to ethical misuse in , where fMRI is employed to predict consumer preferences, raising concerns about manipulation of responses without adequate or . Such applications highlight the potential for commercial exploitation, prompting calls for stricter oversight to prevent undue influence on personal . Reproducibility challenges undermine the reliability of functional imaging findings and contribute to systemic biases in the literature. Task-based fMRI, for example, often exhibits low test-retest reliability, with coefficients (ICCs) frequently below 0.5—indicating less than 50% consistency across sessions—due to factors like physiological noise and task variability. Compounding this is , where studies reporting positive or significant activations are more likely to be published, skewing meta-analyses and inflating perceived effect sizes in fields like . These issues necessitate larger sample sizes and standardized protocols to enhance trustworthiness. Regulatory frameworks aim to address these concerns by establishing standards for clinical and research applications. In the United States, the FDA has cleared specific fMRI software and devices for presurgical , such as tools for identifying eloquent in or tumor cases, ensuring safety and efficacy in therapeutic contexts. Professional societies like the International Society for Magnetic Resonance in Medicine (ISMRM) provide ethical guidelines emphasizing , data handling, and equitable access, promoting responsible practices across global research communities.

References

  1. [1]
    Functional Imaging and Related Techniques - PubMed Central - NIH
    Functional neuroimaging techniques can be used to determine the effects of brain injury or disease on brain systems related to cognition and behavior.
  2. [2]
    Functional Imaging - an overview | ScienceDirect Topics
    Functional imaging is defined as a set of technologies, including positron emission tomography (PET) and single-photon emission computed tomography (SPECT), ...
  3. [3]
    Functional Imaging - an overview | ScienceDirect Topics
    Functional imaging refers to a set of techniques such as PET, MEG, and fMRI used by clinicians and researchers to understand brain anatomy, physiology, ...
  4. [4]
    Using structural and functional brain imaging to uncover how ... - NIH
    Structural imaging refers to approaches that are specialized for the visualization and analysis of anatomical properties of the brain. Structural approaches are ...
  5. [5]
    Brain Imaging: What Are the Different Types? - Brainline.org
    Apr 22, 2011 · structural imaging – deals with the structure of the brain and the ... functional imaging – measures an aspect of brain function, often ...
  6. [6]
    Functional Neuroimaging - an overview | ScienceDirect Topics
    Functional neuroimaging is defined as a set of techniques used to assess brain function by measuring changes in brain activity during specific tasks or at ...
  7. [7]
    Cardiac functional imaging - ScienceDirect.com
    Dynamic functional imaging is obtained by acquiring images of the heart at different phases of the cardiac cycle, allowing assessment of cardiac motion, ...Missing: neuroscience | Show results with:neuroscience
  8. [8]
    Functional imaging - Scholarpedia
    Oct 21, 2011 · Functional imaging is the study of human brain function based on analysis of data acquired using brain imaging modalities.
  9. [9]
    FMRI hemodynamic response function (HRF) as a novel marker of ...
    The hemodynamic response function (HRF) represents the transfer function linking neural activity with the functional MRI (fMRI) signal, modeling neurovascular ...
  10. [10]
    Coupling between neuronal activity and microcirculation
    This review covers what seem to us to be important aspects of the coupling between neuronal activity and haemodynamics from the neuroimaging perspective.
  11. [11]
    Functional Connectivity - an overview | ScienceDirect Topics
    Functional connectivity is the coordinated information exchange of spatially separated brain regions in order to allow integrative and higher-order functions.
  12. [12]
    Brain Structural and Functional Connectivity: A Review of Combined ...
    Functional connectivity refers to statistical dependence between time series of electro-physiological activity and (de)oxygenated blood levels in distinct ...Introduction · Spectral Analysis – Local... · Synchronization Analysis... · Discussion
  13. [13]
    History of International Society for Cerebral Blood Flow and ... - NIH
    Dec 21, 2011 · Quantitative methods for measurements of cerebral blood flow (CBF) and energy metabolism became available in the middle of the 20th century and ...
  14. [14]
    Behind the scenes of functional brain imaging: A historical and ... - NIH
    A brief historical review of work on the relationships between brain function, blood flow, and metabolism is included.
  15. [15]
    The history of cerebral PET scanning: From physiology to ... - NIH
    Mar 5, 2013 · The first large-scale use of a human positron imaging device was developed by physicist Gordon Brownell and neurosurgeon William Sweet at the ...
  16. [16]
    The History of Positron Emission Tomography - ScienceDirect.com
    Gordon L. Brownell (Figure 1) along with William Sweet (Figure 2) and the physics group at MGH developed and built this first brain probe using 2 opposing ...
  17. [17]
    Record of a Single fMRI Experiment in May of 1991 - PMC
    Aug 3, 2011 · The discovery of BOLD fMRI at MGH in May 1991 was 1) built on the on-going effort to develop new MR techniques for perfusion measurement with intrinsic blood ...
  18. [18]
    A History of fMRI | MGH/HST Martinos Center for Biomedical Imaging
    1991 was an extraordinary year in the development of magnetic resonance imaging (MRI). In the space of a few days in August – at the 10th annual meeting.
  19. [19]
    Resting-State Functional Brain Connectivity - Sage Journals
    Sep 10, 2013 · Recent resting-state studies using fMRI and EEG/MEG have consistently demonstrated that the spontaneous functional connectivity network ...
  20. [20]
    Non-invasive neuroimaging using near-infrared light - ScienceDirect
    This article reviews diffuse optical brain imaging, a technique that employs near-infrared light to non-invasively probe the brain for changes in parameters ...
  21. [21]
    Integrating Functional MRI and EEG/MEG - PMC - PubMed Central
    Multimodal fMRI-EEG/MEG integrated neuroimaging approaches hold the potential to greatly enhance our ability to reveal the brain functional connectivity.
  22. [22]
    A Brief History of the Resting State: the Washington University ... - NIH
    We present a history of the concepts and developments that have led us to focus on the resting state as an object of study.
  23. [23]
    The neural basis of functional brain imaging signals - ScienceDirect
    The evidence supports a conceptual shift in the mechanisms of neurovascular coupling, from a unidimensional process involving neuronal-astrocytic signaling to ...Missing: seminal papers
  24. [24]
    Investigating Human Neurovascular Coupling Using Functional ...
    The mechanisms that link a transient neural activity to the corresponding increase of cerebral blood flow (CBF) are termed neurovascular coupling (NVC).
  25. [25]
    Dynamics of blood flow and oxygenation changes during brain ...
    Dec 12, 2005 · Dynamics of blood flow and oxygenation changes during brain activation: The balloon model. Richard B. Buxton Ph.D.,. Corresponding Author.
  26. [26]
    Dynamics of blood flow and oxygenation changes during ... - PubMed
    Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn Reson Med. 1998 Jun;39(6):855-64. ... Authors. R B Buxton ...Missing: paper URL
  27. [27]
    MRI-based methods for quantification of the cerebral metabolic rate ...
    All MRI-based CMRO 2 methods are based on modeling the effect of paramagnetic deoxyhemoglobin on the magnetic resonance signal.
  28. [28]
    The physiological and biochemical bases of functional brain imaging
    Functional brain imaging is based on the display of computer-derived images of changes in physiological and/or biochemical functions altered by activation ...
  29. [29]
    Lactate muscles its way into consciousness: fueling brain activation
    The possibility that working astrocytes have a major role in energetics of brain activation and metabolic brain imaging is important because astrocytes are ...
  30. [30]
    Functional connectivity in the motor cortex of resting human brain ...
    An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that ...
  31. [31]
    Correlated Low-Frequency BOLD Fluctuations in the Resting ...
    The resting brain is associated with significant intrinsic activity fluctuations, such as the correlated low-frequency (LF) blood oxygen level–dependent (BOLD) ...
  32. [32]
    Functional Cardiac Magnetic Resonance Imaging (MRI) in the ...
    Functional MRI has become increasingly investigated as a noninvasive method for assessing myocardial viability and perfusion. Most patients in the published ...
  33. [33]
    Physiological Basis of Myocardial Contrast Enhancement in Fast ...
    In contrast-enhanced MR images of 2-day-old reperfused canine infarcts, myocardial regions of hypoenhancement are related to the no-reflow phenomenon.Missing: mismatch | Show results with:mismatch
  34. [34]
    What does fMRI measure? | Brain Metrics | Learn Science at Scitable
    May 16, 2015 · fMRI measures the indirect consequences of neural activity, specifically the haemodynamic response, linked to changes in blood oxygen levels.
  35. [35]
    The physics of functional magnetic resonance imaging (fMRI) - PMC
    Functional magnetic resonance imaging (fMRI) is a methodology for detecting dynamic patterns of activity in the working human brain.
  36. [36]
    How Brain Activity Is Measured | Psychology Today
    Unlike fMRI, which provides an indirect impression of brain cell activity based on blood flow, electroencephalography (EEG) directly measures electrical ...
  37. [37]
    Spatiotemporal Precision of Neuroimaging in Psychiatry
    Apr 15, 2023 · One relates to a trade-off between spatial and temporal resolutions that is inherent to a reliance on noninvasive neuroimaging approaches. This ...
  38. [38]
    Lowering the thermal noise barrier in functional brain mapping with ...
    Aug 30, 2021 · Thermal noise associated with the MR detection, arising either from the electronics and/or the sample, is an important noise source in fMRI and ...
  39. [39]
    On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise ...
    SNR compares the global signal level to the amount of noise and can be applied to either MRI images or task-related and resting-state fMRI (e.g. tSNR).
  40. [40]
    Characterization of the BOLD signal in functional MRI
    Sep 2, 2024 · ... BOLD signal change at 3T is typically in the order of a few percent. Furthermore, the coupling between BOLD signal changes and neuronal ...
  41. [41]
    Benefits and Risks - FDA
    Dec 9, 2017 · MR images are made without using any ionizing radiation, so patients are not exposed to the harmful effects of ionizing radiation. But while ...Missing: functional | Show results with:functional
  42. [42]
    Safety Considerations in Magnetic Resonance Imaging of Patients ...
    Oct 1, 2016 · Strong magnetic fields formed by MRI scanners pose a risk to patients with ferromagnetic and electronic implanted devices. This is an important ...
  43. [43]
    https://www.sciencedirect.com/science/article/pii/S0006322322015256
  44. [44]
    https://www.nature.com/articles/s41467-021-25431-8
  45. [45]
    Overview of Functional Magnetic Resonance Imaging - PMC - NIH
    Functional Magnetic Resonance Imaging (fMRI) is a class of imaging methods developed in order to demonstrate regional, time-varying changes in brain metabolism.Basis For Fmri · Figure 1 · The Fmri Experiment
  46. [46]
    https://openarchive.ki.se/articles/thesis/Characterization_of_the_BOLD_signal_in_functional_MRI/26898601
  47. [47]
    Positron emission tomography: An overview - PMC - NIH
    As already described, in a positron annihilation two photons are simultaneously emitted in opposite directions. If either photon is absorbed within the body ...
  48. [48]
    PET Scanning - StatPearls - NCBI Bookshelf - NIH
    Feb 26, 2025 · The typical radiation dose from 18F-FDG is approximately 7.5 mSv.[1] For FDG PET-CT, the radiation dose typically ranges from 14 to 30 mSv, ...
  49. [49]
    Understanding the Standardized Uptake Value, Its Methods, and ...
    Sep 1, 2004 · Somewhat analogously, in PET the standardized uptake value (SUV) came to be used as a tool to supplement visual interpretation.
  50. [50]
    Introduction - Electroencephalography (EEG): An Introductory ... - NCBI
    The neural activity detectable by the EEG is the summation of the excitatory and inhibitory postsynaptic potentials of relatively large groups of neurons firing ...Missing: summed seminal
  51. [51]
    Electroencephalography - ScienceDirect.com
    Feb 4, 2019 · To be clear, EEG does not measure action potentials, but rather postsynaptic potentials. Action potentials are the rapid current flow from ...Main Text · Eeg As A Neuroimaging... · Brain Decoding And...Missing: summed seminal
  52. [52]
    History and Evolution of the Electroencephalogram - PMC
    Aug 7, 2024 · Jasper developed the 10-20 electrode placement system, ensuring all brain areas are covered for efficiency in EEG readings. The 10-20 placement ...Introduction And Background · Review · Figure 1. Infographic...
  53. [53]
    EPIC: Annotated epileptic EEG independent components for artifact ...
    Aug 20, 2022 · Frequency filtering. The algorithm filtered the data using a 0.5–100 Hz bandpass 4th-order Butterworth filter and a 50 Hz 2nd-order notch filter ...Methods · Frequency Filtering · Eeg Segmentation
  54. [54]
    Review on solving the inverse problem in EEG source analysis - PMC
    In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight.
  55. [55]
    From SQUIDs to neuroscience Neuroimage 20th Anniversary ...
    This paper provides an overview on the basic principles and applications of magnetoencephalography (MEG), a technique that requires the use of many SQUIDs ...
  56. [56]
    Magnetoencephalography: Basic principles - PMC - NIH
    MEG fields pass through the head without any distortion. This is a significant advantage of MEG over EEG. MEG provides a high spatial and temporal ...
  57. [57]
    Protocol for designing, conducting, and analyzing event-related ...
    May 22, 2025 · In this protocol, we describe steps for designing an ERP experimental task, measuring EEG, preprocessing EEG data in MATLAB, and exporting ERP data for ...Missing: placement | Show results with:placement
  58. [58]
    Rejuvenating classical brain electrophysiology source localization ...
    Mar 12, 2024 · EEG/MEG are non-invasive measurement modalities with high temporal resolution up to 1 ms. EEG/MEG signals can be collected noninvasively ...
  59. [59]
    Day‐to‐day individual alpha frequency variability measured by a ...
    May 3, 2023 · Alpha oscillations (8–12 Hz) were first discovered in the parietal and occipital regions of the brain and are highest in amplitude when the ...
  60. [60]
    Stress monitoring using low-cost electroencephalogram devices
    The review demonstrates that low-cost EEG devices can offer a viable and less invasive alternative to medical-grade systems.
  61. [61]
    A comprehensive study on electroencephalography and ...
    Our results for cortical sources show that EEG sensitivity is higher for radial and deep sources and MEG for tangential ones, which are the majority of sources.Missing: limitations poor
  62. [62]
    Volume conduction effects in EEG and MEG - ResearchGate
    Aug 6, 2025 · Volume conductor models that are commonly used to describe the EEG and MEG neglect holes in the skull, lesions, the ventricles, ...
  63. [63]
    Overview of magnetoencephalography in basic principle, signal ...
    MEG is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from ...Missing: cap placement
  64. [64]
    The present and future use of functional near‐infrared spectroscopy ...
    Sep 7, 2018 · In this review we aim to provide a comprehensive and state‐of‐the‐art review of fNIRS basics, technical developments, and applications.
  65. [65]
    Functional Near-Infrared Spectroscopy and Its Clinical Application in ...
    Jul 8, 2020 · This paper reviews the basic mechanisms of fNIRS and its current clinical applications, the limitations toward more widespread clinical usage of fNIRS,Abstract · Introduction · Clinical Application of fNIRS · fMRI Validation of fNIRS
  66. [66]
    Functional Near Infrared Spectroscopy: Enabling Routine ... - NIH
    Oct 6, 2017 · We review recent advances in signal processing and hardware that significantly improve the capabilities of fNIRS by reducing the impact of ...<|separator|>
  67. [67]
    Functional Ultrasound (fUS) During Awake Brain Surgery - Frontiers
    Jan 8, 2020 · We have applied fUS during conventional awake brain surgery to determine its clinical potential for both intra-operative functional and vascular brain mapping.Missing: seminal | Show results with:seminal
  68. [68]
    Intraoperative Functional Ultrasound Imaging of Human Brain Activity
    Aug 4, 2017 · We introduce a new, portable neuroimaging modality of the human brain based on functional ultrasound (fUS) for deep functional cortical mapping.
  69. [69]
    Functional ultrasound neuroimaging: a review of the preclinical and ...
    In this review, we position ultrasound in the field of neuroimaging and discuss how it complements current tools available to neurobiologists and clinicians.Missing: fUS seminal papers
  70. [70]
    Functional Magnetic Resonance Imaging Methods - PMC - NIH
    In this article, we review the methods used to acquire and analyze fMRI signals. ... Comparison of detrending methods for optimal fMRI preprocessing. Neuroimage ...Missing: seminal | Show results with:seminal
  71. [71]
    Removing the effects of task-related motion using independent ...
    This paper describes a new method of adjusting functional magnetic-resonance imaging (fMRI) time-series data to remove the confounding effects of task-related ...
  72. [72]
    Image-based method for retrospective correction of physiological ...
    The RETROICOR method is demonstrated using resting-state experiments on three subjects and compared with the k-space method. The method is found to perform well ...
  73. [73]
    Slice-timing effects and their correction in functional MRI - PMC - NIH
    Highlights. ▻ Slice acquisition delays can degrade sensitivity of fMRI data analysis. ▻ Slice-timing correction during pre-processing suppresses estimator bias.
  74. [74]
    Effects of spatial smoothing on fMRI group inferences - ScienceDirect
    Spatial smoothing using a gaussian filter kernel is applied to each image to obtain smoothness with an effective FWHM of w in each dimension. Consider a ...
  75. [75]
    Image Distortion Correction in fMRI: A Quantitative Evaluation
    A new robust 3d phase-unwrapping algorithm applied to fMRI field maps for the undistortion of EPIs. NeuroImage, 13 (2001), p. S103. Google Scholar. 10. R ...
  76. [76]
    Using domain knowledge for robust and generalizable deep ...
    Oct 6, 2022 · The main challenge of attenuation correction lies in finding reliable attenuation-correction factors (ACF) compensating for this loss, which is ...Results · Discussion · Decomposition-Based Dl
  77. [77]
    article Extraction of ocular artefacts from EEG using independent ...
    A new approach to the correction of these disturbances is presented using the statistical technique of independent component analysis.
  78. [78]
    A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth ...
    Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically underdiscussed aspect of reproducibility.
  79. [79]
    Analysis of fMRI time-series revisited - PubMed
    This paper presents a general approach to the analysis of functional MRI time-series from one or more subjects.
  80. [80]
    Review The general linear model and fMRI: Does love last forever?
    In this review, we first set out the general linear model (GLM) for the non technical reader, as a tool able to do both linear regression and ANOVA within ...
  81. [81]
    [PDF] An Introduction to Random Field Theory - FIL | UCL
    Mar 4, 2003 · We describe the multiple comparison problem and the usual solution, which is the Bonferroni correction. We explain why spatial correlation in ...
  82. [82]
    Zen and the Art of Multiple Comparisons - PMC - NIH
    In the fMRI community, random field theory (RFT) (8) is the most popular approach for controlling the FWER. Theoretically, it is considerably more complicated ...
  83. [83]
    Resting state fMRI: A review on methods in resting state connectivity ...
    However, the article focuses on the FC analysis techniques such as seed-based analysis, independent component analysis (ICA) and graph theory analysis for ...
  84. [84]
    Graph-based network analysis of resting-state functional MRI
    In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations.
  85. [85]
    Kinetic modeling and parametric imaging with dynamic PET for ...
    The aim of this review is to present some general considerations about dPET analysis in oncology by means of kinetic modeling, based on compartmental and ...
  86. [86]
    Time-frequency analysis methods and their application in ...
    Time-frequency analyses can better characterize the oscillations contained in the EEG data. By separating power and phase information across different ...
  87. [87]
    Circular analysis in systems neuroscience – the dangers of double ...
    In particular, “double dipping” – the use of the same data set for selection and selective analysis – will give distorted descriptive statistics and invalid ...
  88. [88]
    Presurgical Functional Cortical Mapping Using Electromagnetic ...
    Functional cortical mapping (FCM) aims at localizing eloquent functional cortex using a range of invasive and non-invasive methods (1). Its main indication is ...
  89. [89]
    Use of fMRI in the presurgical evaluation of patients with epilepsy
    This article summarizes an American Academy of Neurology (AAN) guideline on use of functional MRI (fMRI) for presurgical mapping in epilepsy.
  90. [90]
    Clinical impact of amyloid PET using 18F-florbetapir in patients ... - NIH
    Oct 4, 2022 · Amyloid PET using 18F-florbetapir PET had a substantial clinical impact on AD and non-AD diagnosis and on patient management by enhancing ...
  91. [91]
    Use of Florbetapir-PET for Imaging β-Amyloid Pathology | Radiology
    Jan 19, 2011 · Context The ability to identify and quantify brain β-amyloid could increase the accuracy of a clinical diagnosis of Alzheimer disease.
  92. [92]
    Imaging Functional Recovery Following Ischemic Stroke - NIH
    This review compares clinical and preclinical fMRI studies of recovery following stroke and focuses on how refinement of preclinical models and MRI methods may ...
  93. [93]
    Resting-state network alterations in depression - NIH
    The meta-analysis revealed significant alterations in RSN connectivity, both within and between networks, in patients with depression compared with healthy ...
  94. [94]
    The Neurophysiology of Auditory Hallucinations – A Historical ... - NIH
    May 16, 2011 · ... MEG, auditory hallucination, corollary discharge, superior temporal cortex, schizophrenia, psychosis ... clinical symptoms of schizophrenia ...
  95. [95]
    Role of PET-CT in the assessment of myocardial viability in patients ...
    Viability assessment should be performed in patients who present after 12 h of acute myocardial infarction or with LV dysfunction due to ischemic heart disease.
  96. [96]
    Cerebral Near Infrared Spectroscopy Monitoring in Term Infants With ...
    NIRS can continuously monitor CBF, oxygenation, and metabolism at the cot side from the early stages after birth, with the potential to provide information on ...
  97. [97]
    Bilateral recruitment of prefrontal cortex in working memory ... - PNAS
    The memory paradigm of the main fMRI experiment. Participants worked on delayed match-to-sample WM tasks. The items that had to be remembered in a trial were ...
  98. [98]
    The role of prefrontal cortex in cognitive control and executive function
    Aug 18, 2021 · By combining fMRI methods with the measurement of evoked response potentials (ERP), it is possible to track the time-course of FPN control ...
  99. [99]
    Estimation of the Timing of Human Visual Perception from ...
    To explore the timing and the underlying neural dynamics of visual perception, we analyzed the relationship between the manual reaction time (RT) to the onset ...
  100. [100]
    Developmental Changes in Resting-State Functional Networks ...
    May 15, 2019 · Three ICNs that have been well established and serve distinct roles in cognitive and emotional processing include the default mode network (DMN) ...Missing: graph neurodevelopment
  101. [101]
    Developmental trajectories of the default mode, frontoparietal, and ...
    The default mode (DMN), frontoparietal (FPN), and salience (SN) networks interact to support a range of behaviors, are vulnerable to environmental insults, ...2 Methods · 2.4 Image Processing · 3 Results
  102. [102]
    Development of the whole-brain functional connectome explored via ...
    Oct 18, 2024 · Using resting state FC and graph theory, we have demonstrated age-related developmental trends in multiple graph theory metrics over the course ...Imaging Analysis · Betweenness Centrality · Discussion
  103. [103]
    Imaging dopamine's role in drug abuse and addiction - PMC
    Indeed, imaging studies show that drug abusers have marked decreases in dopamine D2 receptors and in dopamine release.
  104. [104]
    Dopamine in Drug Abuse and Addiction: Results of Imaging Studies ...
    Imaging studies have provided new insights on the role of dopamine (DA) in drug abuse and addiction in the human brain.
  105. [105]
    Pharmacologic approaches to cerebral aging and neuroplasticity
    Apr 1, 2022 · We review in this article the main aspects of human brain plasticity as shown in patients with stroke, the drug modulation of brain plasticity ...
  106. [106]
    Functional ultrasound imaging: A useful tool for ... - ScienceDirect.com
    Dec 15, 2021 · First, fUS can be used to study intrinsic functional connectivity, namely patterns of correlated activity between brain regions. In this area, ...
  107. [107]
    Chronic brain functional ultrasound imaging in freely moving rodents ...
    Functional ultrasound imaging (fUS) is an emerging imaging technique that indirectly measures neural activity via changes in blood volume.
  108. [108]
    Near-infrared spectroscopy: recent advances in infant speech ...
    NIRS has become a reliable, easy-to-use and efficient tool to explore the linguistic and cognitive abilities of neonates and young infants.
  109. [109]
    Prerequisites of language acquisition in the newborn brain
    Mar 27, 2023 · Here we review cognitive abilities of newborn infants that promote language acquisition, focusing primarily on studies tapping neural activity.
  110. [110]
    The WU-Minn Human Connectome Project: An Overview - PMC
    A systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults.
  111. [111]
    The Human Connectome Project: A retrospective - ScienceDirect.com
    Dec 1, 2021 · The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain ...
  112. [112]
    A Hitchhiker's Guide to Functional Magnetic Resonance Imaging
    A practical description of the parameters involved in a typical fMRI ... At 3 T fields, typical voxel sizes range between 2.8 and 3.5 mm3 (Wald, 2012 ...
  113. [113]
    Spatial and temporal resolutions of EEG: Is it really black and white ...
    Such volume–conduction-induced mixture is the main cause of the poor spatial resolution of scalp EEG (around 5 to 9 cm, Nunez et al., 1994; Babiloni et al., ...
  114. [114]
    The effects of hemodynamic lag on functional connectivity and ...
    Our data reveal that hemodynamic delay is common sub-acutely, alters functional connectivity, and may be of clinical importance. Keywords: Acute stroke, brain ...Missing: misalignment false
  115. [115]
    The voxel-wise analysis of false negative fMRI activation in regions ...
    May 6, 2019 · In healthy subjects, false negative BOLD fMRI signal activation can be found when cerebrovascular reactivity is artificially impaired. Our ...Missing: lag misalignment
  116. [116]
    A dedicated paediatric [18F]FDG PET/CT dosage regimen - PMC - NIH
    Jul 19, 2021 · To reduce this risk, the radiation dose of paediatric PET/CT scans should be as low as reasonably achievable (ALARA) [4] with acceptable image ...
  117. [117]
    Scalp and skull influence on near infrared photon propagation in the ...
    Jan 15, 2014 · In this model, sensitivity to brain tissue decreased as skull thickness increased, with an 80% reduction in NIRS signal intensity when the skull ...
  118. [118]
    Variation of BOLD hemodynamic responses across subjects and ...
    The variability in the shape or magnitude of HRFs across subjects and brain regions may arise from multiple factors including neural activity differences, ...
  119. [119]
    BOLD Hemodynamic Response Function Changes Significantly with ...
    The hemodynamic response function (HRF) characterizes changes in BOLD signal over time, typically peaking around three to five seconds after stimulus ...
  120. [120]
    The confound of hemodynamic response function variability in ...
    We argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group ...
  121. [121]
    Computational approaches to fMRI analysis - PMC - PubMed Central
    Feb 23, 2017 · Given these challenges, the models fitted to fMRI data need constraints to help them to find the 'needles' of signal embedded in the much ...
  122. [122]
    The statistical challenge of finding spontaneous changes ... - bioRxiv
    Dec 15, 2020 · The statistical challenge of finding spontaneous changes in functional connectivity in high-dimensional fMRI data. Diego Vidaurre. doi: https ...