Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions

The Contribution of Functional Magnetic Resonance Imaging to the Understanding of the Effects of Acute Physical Exercise on Cognition

The fact that a single bout of acute physical exercise has a positive impact on cognition is well-established in the literature, but the neural correlates that underlie these cognitive improvements are not well understood. Here, the use of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), offers great potential, which is just starting to be recognized. This review aims at providing an overview of those studies that used fMRI to investigate the effects of acute physical exercises on cerebral hemodynamics and cognition. To this end, a systematic literature survey was conducted by two independent reviewers across five electronic databases. The search returned 668 studies, of which 14 studies met the inclusion criteria and were analyzed in this systematic review. Although the findings of the reviewed studies suggest that acute physical exercise (e.g., cycling) leads to profound changes in functional brain activation, the small number of available studies and the great variability in the study protocols limits the conclusions that can be drawn with certainty. In order to overcome these limitations, new, more well-designed trials are needed that (i) use a more rigorous study design, (ii) apply more sophisticated filter methods in fMRI data analysis, (iii) describe the applied processing steps of fMRI data analysis in more detail, and (iv) provide a more precise exercise prescription.

Structural mediation of human brain activity revealed by white-matter interpolation of fMRI

Understanding how the anatomy of the human brain constrains and influences the formation of large-scale functional networks remains a fundamental question in neuroscience. Here, given measured brain activity in gray matter, we interpolate these functional signals into the white matter on a structurally-informed high-resolution voxel-level brain grid. The interpolated volumes reflect the underlying anatomical information, revealing white matter structures that mediate the interaction between temporally coherent gray matter regions. Functional connectivity analyses of the interpolated volumes reveal an enriched picture of the default mode network (DMN) and its subcomponents, including the different white matter bundles that are implicated in their formation, thus extending currently known spatial patterns that are limited within the gray matter only. These subcomponents have distinct structure-function patterns, each of which are differentially observed during tasks, demonstrating plausible structural mechanisms for functional switching between task-positive and -negative components. This work opens new avenues for the integration of brain structure and function, and demonstrates the collective mediation of white matter pathways across short and long-distance functional connections.

The Organization of the Human Corpus Callosum Estimated by Intrinsic Functional Connectivity with White-Matter Functional Networks

The corpus callosum is the commissural bridge of white-matter bundles important for the human brain functions. Previous studies have analyzed the structural links between cortical gray-matter networks and subregions of corpus callosum. While meaningful white-matter functional networks (WM-FNs) were recently reported, how these networks functionally link with distinct subregions of corpus callosum remained unknown. The current study used resting-state functional magnetic resonance imaging of the Human Connectome Project test–retest data to identify 10 cerebral WM-FNs in 119 healthy subjects and then parcellated the corpus callosum into distinct subregions based on the functional connectivity between each callosal voxel and above networks. Our results demonstrated the reproducible identification of WM-FNs and their links with known gray-matter functional networks across two runs. Furthermore, we identified reliably parcellated subregions of the corpus callosum, which might be involved in primary and higher order functional systems by functionally connecting with WM-FNs. The current study extended our knowledge about the white-matter functional signals to the intrinsic functional organization of human corpus callosum, which could help researchers understand the neural substrates underlying normal interhemispheric functional connectivity as well as dysfunctions in various mental disorders.

Predicting trait-like individual differences in fear of pain in the healthy state using gray matter volume

Fear of pain (FOP) can be considered as a product of evolution from overstated negative interpretations of pain and sometimes may cause more damage than the actual pain itself. While trait-like measures of FOP have emerged as predictors for the inception and development of chronic pain, its neural underpinnings are not well understood. To investigate the relationship between gray matter volumes (GMV) and trait-like individual differences in FOP, we analyzed structural magnetic resonance imaging data in a sample of healthy young adults. Regression analysis results showed that individuals with higher FOP scores displayed higher GMV in brain regions important for the regulation of pain and fear. These brain areas include the pregenual anterior cingulate cortex (ACC), the anterior part of the dorsal ACC, the dorsomedial prefrontal cortex, and the adjacent pre-supplementary motor area. Furthermore, cross-validation analysis confirmed that the identified regional GMV offered a reliable neural signature of trait-like FOP. Our findings shed more light on the neuroanatomical architecture of FOP in currently pain-free people, which may be helpful to guide early interventions to prevent FOP from becoming chronic.

Aberrant interhemispheric functional connectivity in first-episode, drug-naïve major depressive disorder

Many studies have indicated that depression is associated with impairment of the topological organization of the brain functional network, which may lead to disruption of mood and cognition in depressive patients. The abnormality of homotopic connectivity provides a basis for the clinical manifestations of depression, such as emotional and cognitive disorders. Several studies have investigated the abnormal imbalance of homotopic regions between the hemispheres in depressive patients. However, the reported findings are inconsistent. Additionally, the published studies have focused on only the grey matter when investigating functional connectivity abnormalities of the bilateral cerebral hemispheres in major depressive disorder (MDD). The aim of this study is to investigate functional connectivity abnormalities of the bilateral cerebral hemispheres in patients with first-episode, drug-naïve MDD using a voxel-mirrored homotopic connectivity (VMHC) method. Based on DSM-IV diagnostic criteria, 23 first-episode, drug-naïve MDD patients were recruited, together with 20 gender- and age-matched healthy normal controls. A Philips Achieva 3.0 T MRI scanner was used to acquire brain functional images at resting state as well as high-resolution structural images. The functional images were preprocessed by using Data Processing Assistant for Resting-State Functional MR Imaging toolkit and SPM8.VMHC between the bilateral hemispheres was computed and compared between the MDD and control groups. The correlation between the VMHC values of the abnormal homotopy function areas and the Hamilton Depression Rating Scale (HAMD) was evaluated in the MDD patients. Compared with the control group, the MDD patients showed significantly decreased VMHC values in the bilateral brain regions including the insular, putamen, and frontal white matter. The MDD patients did not exhibit increased VMHC values in any brain regions compared with the normal controls. In addition, a negative correlation was observed between the VMHC value in the frontal lobe white-matter and the HAMD in the MDD patients. Abnormalities in brain homotopic functional connectivity observed in this study may indicate abnormal neural circuits related to aberrant cognition and emotional processing in MDD. Although the physiological significance underlaying abnormal VMHC in white matter in the frontal lobe needs further research, our study new angle to investigate the role of white-matter abnormalities in MDD as well as other psychiatric disorders.

Resting-state connectivity stratifies premanifest Huntington's disease by longitudinal cognitive decline rate

Patient stratification is critical for the sensitivity of clinical trials at early stages of neurodegenerative disorders. In Huntington’s disease (HD), genetic tests make cognitive, motor and brain imaging measurements possible before symptom manifestation (pre-HD). We evaluated pre-HD stratification models based on single visit resting-state functional MRI (rs-fMRI) data that assess observed longitudinal motor and cognitive change rates from the multisite Track-On HD cohort (74 pre-HD, 79 control participants). We computed longitudinal performance change on 10 tasks (including visits from the preceding TRACK-HD study when available), as well as functional connectivity density (FCD) maps in single rs-fMRI visits, which showed high test-retest reliability. We assigned pre-HD subjects to subgroups of fast, intermediate, and slow change along single tasks or combinations of them, correcting for expectations based on aging; and trained FCD-based classifiers to distinguish fast- from slow-progressing individuals. For robustness, models were validated across imaging sites. Stratification models distinguished fast- from slow-changing participants and provided continuous assessments of decline applicable to the whole pre-HD population, relying on previously-neglected white matter functional signals. These results suggest novel correlates of early deterioration and a robust stratification strategy where a single MRI measurement provides an estimate of multiple ongoing longitudinal changes.

Impaired interactions among white-matter functional networks in antipsychotic-naive first-episode schizophrenia

Schizophrenia has been conceptualized as a disorder arising from structurally pathological alterations to white-matter fibers in the brain. However, few studies have focused on white-matter functional changes in schizophrenia. Considering that converging evidence suggests that white-matter resting state functional MRI (rsfMRI) signals can effectively depict neuronal activity and psychopathological status, this study examined white-matter network-level interactions in antipsychotic-naive first-episode schizophrenia (FES) to facilitate the interpretation of the psychiatric pathological mechanisms in schizophrenia. We recruited 42 FES patients (FESs) and 38 healthy controls (HCs), all of whom underwent rsfMRI. We identified 11 white-matter functional networks, which could be further classified into deep, middle, and superficial layers of networks. We then examined network-level interactions among these 11 white-matter functional networks using coefficient Granger causality analysis. We employed group comparisons on the influences among 11 networks using network-based statistic. Excitatory influences from the middle superior corona radiate network to the superficial orbitofrontal and deep networks were disrupted in FESs compared with HCs. Additionally, an extra failure of suppression within superficial networks (including the frontoparietal network, temporofrontal network, and the orbitofrontal network) was observed in FESs. We additionally recruited an independent cohort (13 FESs and 13 HCs) from another center to examine the replicability of our findings across centers. Similar replication results further verified the white-matter functional network interaction model of schizophrenia. The novel findings of impaired interactions among white-matter functional networks in schizophrenia indicate that the pathophysiology of schizophrenia may also lie in white-matter functional abnormalities.

Functional Alterations of White Matter in Chronic Never-Treated and Treated Schizophrenia Patients

BACKGROUND

Schizophrenia is one of the most severe psychiatric disorders and dysfunction of gray matter (GM) has been usually investigated by resting-state functional (f)MRI. However, functional organization of white matter (WM) in chronic schizophrenia remains unclear.

PURPOSE

To investigate the WM functional alterations in chronic never-treated schizophrenia and the effects of long-term antipsychotic treatment.

STUDY TYPE

Prospective.

SUBJECTS

Twenty-five never-treated, 41 matched antipsychotic-treated schizophrenia, and 25 healthy comparison subjects.

FIELD STRENGTH/SEQUENCE

Resting state (rs)-fMRI, T₁ -weighted images (T₁ WI), and diffusion tensor imaging (DTI) covering the whole brain were acquired with a 3.0T scanner.

ASSESSMENT

Amplitude of low-frequency fluctuations (ALFF) in WM and the correlation coefficients between WM and GM were examined and compared among the three participant groups by two reviewers independently. Independent component analysis (ICA) was added to evaluate WM-fMRI signals. Statistical Tests: Analysis of covariance (ANCOVA); Pearson correlation analysis.

RESULTS

Never-treated patients demonstrated lower ALFF in splenium of corpus callosum (SCC) relative to treated patients and controls (P < 0.001, false discovery rate [FDR]-corrected). While the extracted independent component also located in SCC and showed significantly decreased connectivity in never-treated patients when compared to controls (P < 0.05, FDR-corrected). The correlation coefficients of WM-GM displayed greater reductions in the genu of corpus callosum (GCC), pontine crossing tract (PC), bilateral cingulum (hippocampus) (CGH), and bilateral corticospinal tract (CST) in treated patients relative to controls (P < 0.05, FDR-corrected).

DATA CONCLUSION

These findings provide new insight into WM functional alterations over the long-term course of schizophrenia with and without the potential effects of antipsychotic medication. Functional change and abnormal connectivity in SCC were both found greater in untreated patients than treated patients relative to healthy controls, suggesting that long-term antipsychotic treatment may show some protective effects on WM functional organization.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:752-763.

Resting State BOLD Variability Is Linked to White Matter Vascular Burden in Healthy Aging but Not in Older Adults With Subjective Cognitive Decline

Background: Alzheimer's disease (AD) is the leading cause of dementia. A lack of curative treatments and a rapidly aging global population have amplified the need for early biomarkers of the disease process. Recent advances suggest that subjective cognitive decline (SCD) may be one of the earliest symptomatic markers of the AD cascade. Previous studies have identified changes in variability in the blood-oxygen-level-dependent (BOLD) signal in patients with AD, with a possible association between BOLD variability and cerebrovascular factors in the aging brain. The objective of the current study was to determine whether changes in BOLD variability can be identified in individuals with SCD, and whether this signal may be associated with markers of cerebrovascular integrity in SCD and older adults without memory complaints. Method: Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database from 19 participants with SCD and 19 similarly-aged controls. For each participant, a map of BOLD signal variability (SDBOLD) was computed as the standard deviation of the BOLD time-series at each voxel. Group comparisons were performed to examine differences in resting-state SDBOLD in SCD vs. healthy controls. Relationships were then examined between participant SDBOLD maps and neuroimaging markers of white matter vascular infarcts in each group separately. Results: Between-group comparisons showed no significant differences in whole-brain SDBOLD in individuals with SCD and controls. In the healthy aging group, higher white matter hyperintensity (WMH) burden was associated with greater SDBOLD in right temporal regions (p < 0.05), and lower scores on a measure of global executive functioning. These associations were not identified in individuals with SCD. Conclusion: The current study underscores previous evidence for a relationship between SDBOLD and white matter vascular infarcts in the healthy aging brain. The findings also provide evidence for a dissociable relationship between healthy aging and SCD, such that in healthy controls, increased WMH is associated with declines in executive function that is not observed in older adults who present with memory complaints. Further multimodal work is needed to better understand the contributions of vascular pathology to the BOLD signal, and its potential relationship with pathological aging.

Exploring the functional connectome in white matter

A major challenge in neuroscience is understanding how brain function emerges from the connectome. Most current methods have focused on quantifying functional connectomes in gray-matter (GM) signals obtained from functional magnetic resonance imaging (fMRI), while signals from white-matter (WM) have generally been excluded as noise. In this study, we derived a functional connectome from WM resting-state blood-oxygen-level-dependent (BOLD)-fMRI signals from a large cohort (n = 488). The WM functional connectome exhibited weak small-world topology and nonrandom modularity. We also found a long-term (i.e., over 10 months) topological reliability, with topological reproducibility within different brain parcellation strategies, spatial distance effect, global and cerebrospinal fluid signals regression or not. Furthermore, the small-worldness was positively correlated with individuals' intelligence values (r = .17, pcorrected = .0009). The current findings offer initial evidence using WM connectome and present additional measures by which to uncover WM functional information in both healthy individuals and in cases of clinical disease.

White Matter fMRI Activation Cannot Be Treated as a Nuisance Regressor: Overcoming a Historical Blind Spot

Despite past controversies, increasing evidence has led to acceptance that white matter activity is detectable using functional magnetic resonance imaging (fMRI). In spite of this, advanced analytic methods continue to be published that reinforce a historic bias against white matter activation by using it as a nuisance regressor. It is important that contemporary analyses overcome this blind spot in whole brain functional imaging, both to ensure that newly developed noise regression techniques are accurate, and to ensure that white matter, a vital and understudied part of the brain, is not ignored in functional neuroimaging studies.

Altered Resting State Functional Activity and Microstructure of the White Matter in Migraine With Aura

Introduction: Brain structure and function were reported to be altered in migraine. Importantly our earlier results showed that white matter diffusion abnormalities and resting state functional activity were affected differently in the two subtypes of the disease, migraine with and without aura. Resting fluctuation of the BOLD signal in the white matter was reported recently. The question arising whether the white matter activity, that is strongly coupled with gray matter activity is also perturbed differentially in the two subtypes of the disease and if so, is it related to the microstructural alterations of the white matter.

Methods: Resting state fMRI, 60 directional DTI images and high-resolution T1 images were obtained from 51 migraine patients and 32 healthy volunteers. The images were pre-processed and the white matter was extracted. Independent component analysis was performed to obtain white matter functional networks. The differential expression of the white matter functional networks in the two subtypes of the disease was investigated with dual-regression approach. The Fourier spectrum of the resting fMRI fluctuations were compared between groups. Voxel-wise correlation was calculated between the resting state functional activity fluctuations and white matter microstructural measures.

Results: Three white matter networks were identified that were expressed differently in migraine with and without aura. Migraineurs with aura showed increased functional connectivity and amplitude of BOLD fluctuation. Fractional anisotropy and radial diffusivity showed strong correlation with the expression of the frontal white matter network in patients with aura.

Discussion: Our study is the first to describe changes in white matter resting state functional activity in migraine with aura, showing correlation with the underlying microstructure. Functional and structural differences between disease subtypes suggest at least partially different pathomechanism, which may necessitate handling of these subtypes as separate entities in further studies.

Dependence of resting-state fMRI fluctuation amplitudes on cerebral cortical orientation relative to the direction of B0 and anatomical axes

Functional magnetic resonance imaging (fMRI) is now capable of sub-millimetre scale measurements over the entire human brain, however with such high resolutions each voxel is influenced by the local fine-scale details of the cerebral cortical vascular anatomy. The cortical vasculature is structured with the pial vessels lying tangentially along the grey matter surface, intracortical diving arterioles and ascending venules running perpendicularly to the surface, and a randomly oriented capillary network within the parenchyma. It is well-known that the amplitude of the blood-oxygenation level dependent (BOLD) signal emanating from a vessel depends on its orientation relative to the B0-field. Thus the vascular geometric hierarchy will impart an orientation dependence to the BOLD signal amplitudes and amplitude differences due to orientation differences constitute a bias for interpreting neuronal activity. Here, we demonstrate a clear effect of cortical orientation to B0 in the resting-state BOLD-fMRI amplitude (quantified as the coefficient of temporal signal variation) for 1.1 mm isotropic data at 7T and 2 mm isotropic at 3T. The maximum bias, i.e. the fluctuation amplitude difference between regions where cortex is perpendicular to vs. parallel to B0, is about +70% at the pial surface at 7T and +11% at 3T. The B0 orientation bias declines with cortical depth, becomes progressively smaller closer to the white matter surface, but then increases again to a local maximum within the white matter just beneath the cortical grey matter, suggesting a distinct tangential network of white matter vessels that also generate a BOLD orientation effect. We further found significant (negative) biases with the cortex orientation to the anterior-posterior anatomical axis of the head: a maximum negative bias of about -30% at the pial surface at 7T and about -13% at 3T. The amount of signal variance explained by the low frequency drift, motion and the respiratory cycle also showed a cortical orientation dependence; only the cardiac cycle induced signal variance was independent of cortical orientation, suggesting that the cardiac induced component of the image time-series fluctuations is not related to a significant change in susceptibility. Although these orientation effects represent a signal bias, and are likely to be a nuisance in high-resolution analyses, they may help characterize the vascular influences on candidate fMRI acquisitions and, thereby, may be exploited to improve the neuronal specificity of fMRI.

Dysfunctional white-matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Benign epilepsy with centrotemporal spikes (BECT) is the most common childhood idiopathic focal epilepsy syndrome, which characterized with white-matter abnormalities in the rolandic cortex. Although diffusion tensor imaging research could characterize white-matter structural architecture, it cannot detect neural activity or white-matter functions. Recent studies demonstrated the functional organization of white-matter by using functional magnetic resonance imaging (fMRI), suggesting that it is feasible to investigate white-matter dysfunctions in BECT. Resting-state fMRI data were collected from 24 new-onset drug-naive (unmedicated [NMED]), 21 medicated (MED) BECT patients, and 27 healthy controls (HC). Several white-matter functional networks were obtained using a clustering analysis on voxel-by-voxel correlation profiles. Subsequently, conventional functional connectivity (FC) was calculated in four frequency sub-bands (Slow-5:0.01-0.027, Slow-4:0.027-0.073, Slow-3:0.073-0.198, and Slow-2:0.198-0.25 Hz). We also employed a functional covariance connectivity (FCC) to estimate the covariant relationship between two white-matter networks based on their correlations with multiple gray-matter regions. Compared with HC, the NMED showed increased FC and/or FCC in rolandic network (RN) and precentral/postcentral network, and decreased FC and/or FCC in dorsal frontal network, while these alterations were not observed in the MED group. Moreover, the changes exhibited frequency-specific properties. Specifically, only two alterations were shared in at least two frequency bands. Most of these alterations were observed in the frequency bands of Slow-3 and Slow-4. This study provided further support on the existence of white-matter functional networks which exhibited frequency-specific properties, and extended abnormalities of rolandic area from the perspective of white-matter dysfunction in BECT.

Changes in white-matter functional network efficiency across the adult lifespan

Previous studies have used resting-state functional MRI (rs-fMRI) and graph-theory approaches to investigate the lifespan trajectory of the topological organization of the gray-matter functional networks. Recent evidences have suggested that rs-fMRI data can also be used to estimate white-matter function, challenging the conventional practice of taking white-matter signals as noise or artifacts. Here, we examined the correlation between age and white-matter functional network efficiency by applying graph-theory to a large sample of rs-fMRI data of 435 participants. We found that age was correlated negatively with both global and local efficiency of the white-matter functional networks. These findings suggest decreasing white-matter functional network efficiency during the aging process, which provides a complement to conventional gray-matter functional network studies.

Test-retest reliability of cerebral blood flow for assessing brain function at rest and during a vigilance task

Arterial spin labeled (ASL) perfusion magnetic resonance imaging (MRI) is increasingly used to assess regional brain activity and cerebrovascular function in both healthy and clinical populations. ASL perfusion imaging provides a quantitative measure of regional brain activity by determining absolute cerebral blood flow (CBF) values at a resting state or during task performance. However, the comparative reliability of these ASL measures is not well characterized. It is also unclear whether the test-retest reliability of absolute CBF or task-induced CBF change measures would be comparable to the reliability of task performance. In this study, fifteen healthy participants were scanned three times in a strictly controlled in-laboratory study while at rest and during performing a simple and reliable psychomotor vigilance test (PVT). The reliability of absolute CBF and task-induced CBF changes was evaluated using the intraclass correlation coefficient (ICC) and compared to that of task performance. Absolute CBF showed excellent test-retest reliability across the three scans for both resting and PVT scans. The reliability of regional absolute CBF was comparable to that of behavioral measures of PVT performance, and was slightly higher during PVT scans as compared with resting scans. Task-induced regional CBF changes demonstrated only poor to moderate reliability across three scans. These findings suggest that absolute CBF measures are more reliable than task-induced CBF changes for characterizing regional brain function, especially for longitudinal and clinical studies.

Characterization of the hemodynamic response function in white matter tracts for event-related fMRI

Accurate estimates of the BOLD hemodynamic response function (HRF) are crucial for the interpretation and analysis of event-related functional MRI data. To date, however, there have been no comprehensive measurements of the HRF in white matter (WM) despite increasing evidence that BOLD signals in WM change after a stimulus. We performed an event-related cognitive task (Stroop color-word interference) to measure the HRF in selected human WM pathways. The task was chosen in order to produce robust, distributed centers of activity throughout the cortex. To measure the HRF in WM, fiber tracts were reconstructed between each pair of activated cortical areas. We observed clear task-specific HRFs with reduced magnitudes, delayed onsets and prolonged initial dips in WM tracts compared with activated grey matter, thus calling for significant changes to current standard models for accurately characterizing the HRFs in WM and for modifications of standard methods of analysis of functional imaging data.

Regional and network properties of white matter function in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder with dysfunction in cortices as well as white matter (WM) tracts. While the changes to WM structure have been extensively investigated in PD, the nature of the functional changes to WM remains unknown. In this study, the regional activity and functional connectivity of WM were compared between PD patients (n = 57) and matched healthy controls (n = 52), based on multimodel magnetic resonance imaging data sets. By tract-based spatial statistical analyses of regional activity, patients showed decreased structural-functional coupling in the left corticospinal tract compared to controls. This tract also displayed abnormally increased functional connectivity within the left post-central gyrus and left putamen in PD patients. At the network level, the WM functional network showed small-worldness in both controls and PD patients, yet it was abnormally increased in the latter group. Based on the features of the WM functional connectome, previously un-evaluated individuals could be classified with fair accuracy (73%) and area under the curve of the receiver operating characteristics (75%). These neuroimaging findings provide direct evidence for WM functional changes in PD, which is crucial to understand the functional role of fiber tracts in the pathology of neural circuits.

Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies-Quantifying noise removal and neural signal preservation

This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data-driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal-to-noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal-noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting-state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR-corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.

Activations in gray and white matter are modulated by uni-manual responses during within and inter-hemispheric transfer: effects of response hand and right-handedness

Because the visual cortices are contra-laterally organized, inter-hemispheric transfer tasks have been used to behaviorally probe how information briefly presented to one hemisphere of the visual cortex is integrated with responses resulting from the ipsi- or contra-lateral motor cortex. By forcing rapid information exchange across diverse regions, these tasks robustly activate not only gray matter regions, but also white matter tracts. It is likely that the response hand itself (dominant or non-dominant) modulates gray and white matter activations during within and inter-hemispheric transfer. Yet the role of uni-manual responses and/or right hand dominance in modulating brain activations during such basic tasks is unclear. Here we investigated how uni-manual responses with either hand modulated activations during a basic visuo-motor task (the established Poffenberger paradigm) alternating between inter- and within-hemispheric transfer conditions. In a large sample of strongly right-handed adults (n = 49), we used a factorial combination of transfer condition [Inter vs. Within] and response hand [Dominant(Right) vs. Non-Dominant (Left)] to discover fMRI-based activations in gray matter, and in narrowly defined white matter tracts. These tracts were identified using a priori probabilistic white matter atlases. Uni-manual responses with the right hand strongly modulated activations in gray matter, and notably in white matter. Furthermore, when responding with the left hand, activations during inter-hemispheric transfer were strongly predicted by the degree of right-hand dominance, with increased right-handedness predicting decreased fMRI activation. Finally, increasing age within the middle-aged sample was associated with a decrease in activations. These results provide novel evidence of complex relationships between uni-manual responses in right-handed subjects, and activations during within- and inter-hemispheric transfer suggest that the organization of the motor system exerts sophisticated functional effects. Moreover, our evidence of activation in white matter tracts is consistent with prior studies, confirming fMRI-detectable white matter activations which are systematically modulated by experimental condition.

Intracortical smoothing of small-voxel fMRI data can provide increased detection power without spatial resolution losses compared to conventional large-voxel fMRI data

Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for kernels of various shapes and sizes, including curved 3D kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the kernel configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several kernel configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a kernel of an appropriate shape to achieve the best performance-thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation.

Blood Vessels and Perivascular Phagocytes of Prefrontal White and Gray Matter in Suicide

Inflammatory processes may contribute to psychiatric disorders and suicide. Earlier, we reported greater densities of perivascular phagocytes in dorsal prefrontal white matter (DPFWM) in suicide than in non-suicide deaths. To distinguish between greater vascularity and greater coverage of vessels by perivascular phagocytes, and to determine whether the excess of perivascular phagocytes is derived from microglia or from non-parenchymal immune cells, we made stereological estimates of vascular surface area density (AVTOTAL) by staining for glucose transporter Glut-1, and the fraction of vascular surface area (AF) immunoreactive (IR) for CD163 (CD163 AF) in dorsal and ventral prefrontal white and gray matter. Manner of death or psychiatric diagnosis showed no association with CD163 AF in any region. Suicide was associated with a lower AVTOTAL compared with non-suicides in DPFWM (p = 0.018) but not with AVTOTAL in the 3 other regions of interest. Thus, the earlier observation of increased density of perivascular phagocytes in DPFWM after suicide cannot be attributed to infiltration by peripheral monocytes or to increased vascularity. Greater AVTOTAL ventrally than dorsally (p = 0.002) was unique to suicide and white matter.

Resting-state white matter-cortical connectivity in non-human primate brain

Numerous studies have used functional magnetic resonance imaging (fMRI) to characterize functional connectivity between cortical regions by analyzing correlations in blood oxygenation level dependent (BOLD) signals in a resting state. However, to date, there have been only a handful of studies reporting resting state BOLD signals in white matter. Nonetheless, a growing number of reports has emerged in recent years suggesting white matter BOLD signals can be reliably detected, though their biophysical origins remain unclear. Moreover, recent studies have identified robust correlations in a resting state between signals from cortex and specific white matter tracts. In order to further validate and interpret these findings, we studied a non-human primate model to investigate resting-state connectivity patterns between parcellated cortical volumes and specific white matter bundles. Our results show that resting-state connectivity patterns between white and gray matter structures are not randomly distributed but share notable similarities with diffusion- and histology-derived anatomic connectivities. This suggests that resting-state BOLD correlations between white matter fiber tracts and the gray matter regions to which they connect are directly related to the anatomic arrangement and density of WM fibers. We also measured how different levels of baseline neural activity, induced by varying levels of anesthesia, modulate these patterns. As anesthesia levels were raised, we observed weakened correlation coefficients between specific white matter tracts and gray matter regions while key features of the connectivity pattern remained similar. Overall, results from this study provide further evidence that neural activity is detectable by BOLD fMRI in both gray and white matter throughout the resting brain. The combined use of gray and white matter functional connectivity could also offer refined full-scale functional parcellation of the entire brain to characterize its functional architecture.

Altered physiological brain variation in drug-resistant epilepsy

INTRODUCTION

Functional magnetic resonance imaging (fMRI) combined with simultaneous electroencephalography (EEG-fMRI) has become a major tool in mapping epilepsy sources. In the absence of detectable epileptiform activity, the resting state fMRI may still detect changes in the blood oxygen level-dependent signal, suggesting intrinsic alterations in the underlying brain physiology.

METHODS

In this study, we used coefficient of variation (CV) of critically sampled 10 Hz ultra-fast fMRI (magnetoencephalography, MREG) signal to compare physiological variance between healthy controls (n = 10) and patients (n = 10) with drug-resistant epilepsy (DRE).

RESULTS

We showed highly significant voxel-level (p < 0.01, TFCE-corrected) increase in the physiological variance in DRE patients. At individual level, the elevations range over three standard deviations (σ) above the control mean (μ) CV_MREG values solely in DRE patients, enabling patient-specific mapping of elevated physiological variance. The most apparent differences in group-level analysis are found on white matter, brainstem, and cerebellum. Respiratory (0.12-0.4 Hz) and very-low-frequency (VLF = 0.009-0.1 Hz) signal variances were most affected.

CONCLUSIONS

The CV_MREG increase was not explained by head motion or physiological cardiorespiratory activity, that is, it seems to be linked to intrinsic physiological pulsations. We suggest that intrinsic brain pulsations play a role in DRE and that critically sampled fMRI may provide a powerful tool for their identification.

Voxel-wise detection of functional networks in white matter

Functional magnetic resonance imaging (fMRI) depicts neural activity in the brain indirectly by measuring blood oxygenation level dependent (BOLD) signals. The majority of fMRI studies have focused on detecting cortical activity in gray matter (GM), but whether functional BOLD signal changes also arise in white matter (WM), and whether neural activities trigger hemodynamic changes in WM similarly to GM, remain controversial, particularly in light of the much lower vascular density in WM. However, BOLD effects in WM are readily detected under hypercapnic challenges, and the number of reports supporting reliable detections of stimulus-induced activations in WM continues to grow. Rather than assume a particular hemodynamic response function, we used a voxel-by-voxel analysis of frequency spectra in WM to detect WM activations under visual stimulation, whose locations were validated with fiber tractography using diffusion tensor imaging (DTI). We demonstrate that specific WM regions are robustly activated in response to visual stimulation, and that regional distributions of WM activation are consistent with fiber pathways reconstructed using DTI. We further examined the variation in the concordance between WM activation and fiber density in groups of different sample sizes, and compared the signal profiles of BOLD time series between resting state and visual stimulation conditions in activated GM as well as activated and non-activated WM regions. Our findings confirm that BOLD signal variations in WM are modulated by neural activity and are detectable with conventional fMRI using appropriate methods, thus offering the potential of expanding functional connectivity measurements throughout the brain.

Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know

Resting-state fMRI was first described by Biswal et al in 1995 and has since then been widely used in both healthy subjects and patients with various neurologic, neurosurgical, and psychiatric disorders. As opposed to paradigm- or task-based functional MR imaging, resting-state fMRI does not require subjects to perform any specific task. The low-frequency oscillations of the resting-state fMRI signal have been shown to relate to the spontaneous neural activity. There are many ways to analyze resting-state fMRI data. In this review article, we will briefly describe a few of these and highlight the advantages and limitations of each. This description is to facilitate the adoption and use of resting-state fMRI in the clinical setting, helping neuroradiologists become familiar with these techniques and applying them for the care of patients with neurologic and psychiatric diseases.

A Strategy to Reduce Bias of Entropy Estimates in Resting-State fMRI Signals

Complexity analysis of resting-state blood oxygen level-dependent (BOLD) signals using entropy methods has attracted considerable attention. However, investigation on the bias of entropy estimates in resting-state functional magnetic resonance imaging (fMRI) signals and a general strategy for selecting entropy parameters is lacking. In this paper, we present a minimizing error approach to reduce the bias of sample entropy (SampEn) and multiscale entropy (MSE) in resting-state fMRI data. The strategy explored a range of parameters that minimized the relative error of SampEn of BOLD signals in cerebrospinal fluids where minimal physiologic information was present, and applied these parameters to calculate SampEn of BOLD signals in gray matter regions. We examined the effect of various parameters on the results of SampEn and MSE analyses of a large normal aging adult cohort (354 healthy subjects aged 21-89 years). The results showed that a tradeoff between pattern length m and tolerance factor r was necessary to maintain the accuracy of SampEn estimates. Furthermore, an increased relative error of SampEn was associated with an increased coefficient of variation in voxel-wise statistics. Overall, the parameters m = 1 and r = 0.20-0.45 provided reliable MSE estimates in short resting-state fMRI signals. For a single-scale SampEn analysis, a wide range of parameters was available with data lengths of at least 97 time points. This study provides a minimization error strategy for future studies on the non-linear analysis of resting-state fMRI signals to account for the bias of entropy estimates.

Effects of Tissue-Specific Functional Magnetic Resonance Imaging Signal Regression on Resting-State Functional Connectivity

Neuroimaging studies typically consider white matter as unchanging in different neural and metabolic states. However, a recent study demonstrated that white matter signal regression (WMSR) produced a similar loss of neurometabolic information to global (whole-brain) signal regression (GSR) in resting-state functional magnetic resonance imaging (R-fMRI) data. This was unexpected as the loss of information would normally be attributed to neural activity within gray matter correlating with the global R-fMRI signal. Indeed, WMSR has been suggested as an alternative to avoid such pitfalls in GSR. To address these concerns about tissue-specific regression in R-fMRI data analysis, we performed GSR, WMSR, and gray matter signal regression (GMSR) on R-fMRI data from the 1000 Functional Connectomes Project. We describe several regional and motion-related differences between different types of regressions. However, the overall effects of concern, particularly network-specific alteration of correlation coefficients, are present for all regressions. This suggests that tissue-specific regression is not an adequate strategy to counter pitfalls of GSR. Conversely, if GSR is desired, but the studied disease state excludes either gray matter or white matter from analysis (e.g., due to tissue atrophy), our results indicate that WMSR or GMSR may reproduce the gross effects of GSR.

Contribution of systemic vascular effects to fMRI activity in white matter

To investigate a potential contribution of systemic physiology to recently reported BOLD fMRI signals in white matter, we compared photo-plethysmography (PPG) and whole-brain fMRI signals recorded simultaneously during long resting-state scans from an overnight sleep study. We found that intermittent drops in the amplitude of the PPG signal exhibited strong and widespread correlations with the fMRI signal, both in white matter (WM) and in gray matter (GM). The WM signal pattern resembled that seen in previous resting-state fMRI studies and closely tracked the location of medullary veins. Its temporal cross-correlation with the PPG amplitude was bipolar, with an early negative value. In GM, the correlation was consistently positive. Consistent with previous studies comparing physiological signals with fMRI, these findings point to a systemic vascular contribution to WM fMRI signals. The PPG drops are interpreted as systemic vasoconstrictive events, possibly related to intermittent increases in sympathetic tone related to fluctuations in arousal state. The counter-intuitive polarity of the WM signal is explained by long blood transit times in the medullary vasculature of WM, which cause blood oxygenation loss and a substantial timing mismatch between blood volume and blood oxygenation effects. A similar mechanism may explain previous findings of negative WM signals around large draining veins during both task- and resting-state fMRI.

Reprint of 'Noise contributions to the fMRI signal: An Overview'

The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.

Functional MRI registration with tissue-specific patch-based functional correlation tensors

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high-resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs-fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole-brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white-matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals using tissue-specific patch-based functional correlation tensors (ts-PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi-channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods.

The impact of psychosis genome-wide associated ZNF804A variation on verbal fluency connectivity

Schizophrenia (SCZ) and bipolar disorder (BD) have high heritability. Genome-wide association studies (GWAS) have identified ZNF804A as a significant risk gene for both illnesses. A validation of this finding at the brain systems-level is imperative as there is still little understanding of how it heightens risk. Based in part on our recent findings of an effect on widespread decreased white matter microstructural fractional anisotropy (putatively a proxy of its integrity), particularly strong in SCZ, we asked whether the risk allele has a detrimental effect on regional brain activation and functional connectivity during a type of cognitive processing which is, together with its neural correlates, impaired in BD and SCZ: verbal fluency. Functional MRI and genotype data was collected from 80 healthy volunteers, and 54 SCZ and 40 BD patients. A standard multifactorial analysis of variance using statistical parametric mapping and significance correction of FWE p < 0.05 was used. We found the GWAS risk allele A was associated with decreased positive functional coupling between the left precentral gyrus/inferior frontal gyrus (i.e. the most highly recruited area for the task) and: 1) the left inferior frontal gyrus, and 2) the left posterior cingulate gyrus, encompassing the precuneus; both as a main effect across controls and psychosis patients. Such association of the risk allele with reduced functional connectivity (with no area where the opposite main effect was detected), converges with findings in other tasks, our previous finding of its widespread impact on brain white matter microstructure, and with the dysconnectivity hypothesis of SCZ.

Detection of synchronous brain activity in white matter tracts at rest and under functional loading

Functional MRI based on blood oxygenation level-dependent (BOLD) contrast is well established as a neuroimaging technique for detecting neural activity in the cortex of the human brain. While detection and characterization of BOLD signals, as well as their electrophysiological and hemodynamic/metabolic origins, have been extensively studied in gray matter (GM), the detection and interpretation of BOLD signals in white matter (WM) remain controversial. We have previously observed that BOLD signals in a resting state reveal structure-specific anisotropic temporal correlations in WM and that external stimuli alter these correlations and permit visualization of task-specific fiber pathways, suggesting variations in WM BOLD signals are related to neural activity. In this study, we provide further strong evidence that BOLD signals in WM reflect neural activities both in a resting state and under functional loading. We demonstrate that BOLD signal waveforms in stimulus-relevant WM pathways are synchronous with the applied stimuli but with various degrees of time delay and that signals in WM pathways exhibit clear task specificity. Furthermore, resting-state signal fluctuations in WM tracts show significant correlations with specific parcellated GM volumes. These observations support the notion that neural activities are encoded in WM circuits similarly to cortical responses.

Adapted estimate of neural activity based on blood-oxygen-level dependent signal by a model-free spatio-temporal clustering analysis

In this study, we detected brain activity by comparing the overall temporal response of the blood oxygen level referring to hemodynamic response with a modeled hemodynamic response (MHR). However, in a conventional analysis by statistical parametric mapping (SPM) method, the MHR is assumed to be a fixed-response function, which may bias the conclusions about brain activation, such as the shapes of the response curve or the different response delays to stimuli. Therefore, to improve detection efficacy, we applied a spatio-temporal clustering analysis (sTCA) to determine the MHR, which is calculated from the prospective voxels with no a priori information about the experiment design. With the sTCA method, these prospective voxels are detected by the feature with the largest temporal clustering within which these voxels react simultaneously, irrespective of where the variant hemodynamic response occurs. This estimated MHR (eMHR) is then applied to search for brain activation. Preliminary results show that the eMHR signal response closely resembles the real signal response of the target area. Moreover, the activation detection using eMHR method is more sensitive for the human visual and motor tasks than that with the canonical hemodynamic response embedded in the SPM analysis as the default MHR (dMHR). The more precise location of brain activation made possible by the improved sensitivity should provide helpful information about the stimulation of neuron activity.

Intact hemisphere and corpus callosum compensate for visuomotor functions after early visual cortex damage

Unilateral damage to the primary visual cortex (V1) leads to clinical blindness in the opposite visual hemifield, yet nonconscious ability to transform unseen visual input into motor output can be retained, a condition known as "blindsight." Here we combined psychophysics, functional magnetic resonance imaging, and tractography to investigate the functional and structural properties that enable the developing brain to partly overcome the effects of early V1 lesion in one blindsight patient. Visual stimuli appeared in either the intact or blind hemifield and simple responses were given with either the left or right hand, thereby creating conditions where visual input and motor output involve the same or opposite hemisphere. When the V1-damaged hemisphere was challenged by incoming visual stimuli, or controlled manual responses to these unseen stimuli, the corpus callosum (CC) dynamically recruited areas in the visual dorsal stream and premotor cortex of the intact hemisphere to compensate for altered visuomotor functions. These compensatory changes in functional brain activity were paralleled by increased connections in posterior regions of the CC, where fibers connecting homologous areas of the parietal cortex course.

On Myelinated Axon Plasticity and Neuronal Circuit Formation and Function

Studies of activity-driven nervous system plasticity have primarily focused on the gray matter. However, MRI-based imaging studies have shown that white matter, primarily composed of myelinated axons, can also be dynamically regulated by activity of the healthy brain. Myelination in the CNS is an ongoing process that starts around birth and continues throughout life. Myelin in the CNS is generated by oligodendrocytes and recent evidence has shown that many aspects of oligodendrocyte development and myelination can be modulated by extrinsic signals including neuronal activity. Because modulation of myelin can, in turn, affect several aspects of conduction, the concept has emerged that activity-regulated myelination represents an important form of nervous system plasticity. Here we review our increasing understanding of how neuronal activity regulates oligodendrocytes and myelinated axons in vivo, with a focus on the timing of relevant processes. We highlight the observations that neuronal activity can rapidly tune axonal diameter, promote re-entry of oligodendrocyte progenitor cells into the cell cycle, or drive their direct differentiation into oligodendrocytes. We suggest that activity-regulated myelin formation and remodeling that significantly change axonal conduction properties are most likely to occur over timescales of days to weeks. Finally, we propose that precise fine-tuning of conduction along already-myelinated axons may also be mediated by alterations to the axon itself. We conclude that future studies need to analyze activity-driven adaptations to both axons and their myelin sheaths to fully understand how myelinated axon plasticity contributes to neuronal circuit formation and function.

Safety and Long-Term Efficacy of AAV4 Gene Therapy in Patients with RPE65 Leber Congenital Amaurosis

The aim of this study was the evaluation of the safety and efficacy of unilateral subretinal injection of the adeno-associated vector (AAV) serotypes 2 and 4 (AAV2/4) RPE65-RPE65 vector in patients with Leber congenital amaurosis (LCA) associated with RPE65 gene deficiency. We evaluated ocular and general tolerance and visual function up to 1 year after vector administration in the most severely affected eye in nine patients with retinal degeneration associated with mutations in the RPE65 gene. Patients received either low (1.22 × 10¹⁰ to 2 × 10¹⁰ vector genomes [vg]) or high (between 3.27 × 10¹⁰ and 4.8 × 10¹⁰ vg) vector doses. An ancillary study, in which six of the original nine patients participated, extended the follow-up period to 2-3.5 years. All patients showed good ophthalmological and general tolerance to the rAAV2/4-RPE65-RPE65 vector. We observed a trend toward improved visual acuity in patients with nystagmus, stabilization and improvement of the visual field, and cortical activation along visual pathways during fMRI analysis. OCT analysis after vector administration revealed no retinal thinning, except in cases of macular detachment. Our findings show that the rAAV2/4.RPE65.RPE65 vector was well tolerated in nine patients with RPE65-associated LCA. Efficacy parameters varied between patients during follow-up.

Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance

In cognitive network neuroscience, the connectivity and community structure of the brain network is related to measures of cognitive performance, like attention and memory. Research in this emerging discipline has largely focused on two measures of connectivity-modularity and flexibility-which, for the most part, have been examined in isolation. The current project investigates the relationship between these two measures of connectivity and how they make separable contribution to predicting individual differences in performance on cognitive tasks. Using resting state fMRI data from 52 young adults, we show that flexibility and modularity are highly negatively correlated. We use a Brodmann parcellation of the fMRI data and a sliding window approach for calculation of the flexibility. We also demonstrate that flexibility and modularity make unique contributions to explain task performance, with a clear result showing that modularity, not flexibility, predicts performance for simple tasks and that flexibility plays a greater role in predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.

Learning-based structurally-guided construction of resting-state functional correlation tensors

Functional magnetic resonance imaging (fMRI) measures changes in blood-oxygenation-level-dependent (BOLD) signals to detect brain activities. It has been recently reported that the spatial correlation patterns of resting-state BOLD signals in the white matter (WM) also give WM information often measured by diffusion tensor imaging (DTI). These correlation patterns can be captured using functional correlation tensor (FCT), which is analogous to the diffusion tensor (DT) obtained from DTI. In this paper, we propose a noise-robust FCT method aiming at further improving its quality, and making it eligible for further neuroscience study. The novel FCT estimation method consists of three major steps: First, we estimate the initial FCT using a patch-based approach for BOLD signal correlation to improve the noise robustness. Second, by utilizing the relationship between functional and diffusion data, we employ a regression forest model to learn the mapping between the initial FCTs and the corresponding DTs using the training data. The learned forest can then be applied to predict the DTI-like tensors given the initial FCTs from the testing fMRI data. Third, we re-estimate the enhanced FCT by utilizing the DTI-like tensors as a feedback guidance to further improve FCT computation. We have demonstrated the utility of our enhanced FCTs in Alzheimer's disease (AD) diagnosis by identifying mild cognitive impairment (MCI) patients from normal subjects.

Evidence for Functional Networks within the Human Brain's White Matter

Investigation of the functional macro-scale organization of the human cortex is fundamental in modern neuroscience. Although numerous studies have identified networks of interacting functional modules in the gray-matter, limited research was directed to the functional organization of the white-matter. Recent studies have demonstrated that the white-matter exhibits blood oxygen level-dependent signal fluctuations similar to those of the gray-matter. Here we used these signal fluctuations to investigate whether the white-matter is organized as functional networks by applying a clustering analysis on resting-state functional MRI (RSfMRI) data from white-matter voxels, in 176 subjects (of both sexes). This analysis indicated the existence of 12 symmetrical white-matter functional networks, corresponding to combinations of white-matter tracts identified by diffusion tensor imaging. Six of the networks included interhemispheric commissural bridges traversing the corpus callosum. Signals in white-matter networks correlated with signals from functional gray-matter networks, providing missing knowledge on how these distributed networks communicate across large distances. These findings were replicated in an independent subject group and were corroborated by seed-based analysis in small groups and individual subjects. The identified white-matter functional atlases and analysis codes are available at http://mind.huji.ac.il/white-matter.aspx Our results demonstrate that the white-matter manifests an intrinsic functional organization as interacting networks of functional modules, similarly to the gray-matter, which can be investigated using RSfMRI. The discovery of functional networks within the white-matter may open new avenues of research in cognitive neuroscience and clinical neuropsychiatry.SIGNIFICANCE STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, enabling identifications of functional modules and networks in the human brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders.

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc.

Functional connectivity and activity of white matter in somatosensory pathways under tactile stimulations

Functional MRI has proven to be effective in detecting neural activity in brain cortices on the basis of blood oxygenation level dependent (BOLD) contrast, but has relatively poor sensitivity for detecting neural activity in white matter. To demonstrate that BOLD signals in white matter are detectable and contain information on neural activity, we stimulated the somatosensory system and examined distributions of BOLD signals in related white matter pathways. The temporal correlation profiles and frequency contents of BOLD signals were compared between stimulation and resting conditions, and between relevant white matter fibers and background regions, as well as between left and right side stimulations. Quantitative analyses show that, overall, MR signals from white matter fiber bundles in the somatosensory system exhibited significantly greater temporal correlations with the primary sensory cortex and greater signal power during tactile stimulations than in a resting state, and were stronger than corresponding measurements for background white matter both during stimulations and in a resting state. The temporal correlation and signal power under stimulation were found to be twice those observed from the same bundle in a resting state, and bore clear relations with the side of stimuli. These indicate that BOLD signals in white matter fibers encode neural activity related to their functional roles connecting cortical volumes, which are detectable with appropriate methods.