Functional MRI activation in white matter during the Symbol Digit Modalities Test

Association of microstructural lesions of the corpus callosum with cognitive impairment in patients with high grade glioma

PURPOSE

Glioblastoma is one of the most common malignant brain tumors. To ensure a treatment that does not only lengthen survival, but also improves preservation of neurocognitive functions, reliable methods to measure changes in neurocognitive abilities at an early stage are necessary. The most direct way to objectify neurocognitive properties is neuropsychological testing. Neurocognitive decline is often based on lesions of the connectome. We take the corpus callosum (CC) as a reliable structure to identify decline of white matter (WM) integrity. We hypothesized a relation between compromised structural integrity in specific regions of the CC and neurocognitive deficits in glioma patients.

METHODS

We included 28 patients with high-grade glioma who underwent a neuropsychological test battery and MRI with Diffusion tensor imaging (DTI) preoperatively. MRI data was processed using the software fsl, Oxford. Neuropsychological parameters were correlated with the fractional anisotropy (FA) in three parts of the CC.

RESULTS

Preoperatively, most of the neuropsychological parameters correlated significantly with FA of at least one of the CC volumes. Higher FA-values were associated with better focus, memory, speed and speech fluency. Different tests examined the same neuropsychological parameter and then correlated with the same region of the CC.

CONCLUSIONS

We consider the FA of the CC for an adequate parameter to examine the influence of distant lesions on neurocognitive abilities.

Sex differences in patterns of white matter neuroplasticity after balance training in young adults

Introduction

In past work we demonstrated different patterns of white matter (WM) plasticity in females versus males associated with learning a lab-based unilateral motor skill. However, this work was completed in neurologically intact older adults. The current manuscript sought to replicate and expand upon these WM findings in two ways: (1) we investigated biological sex differences in neurologically intact young adults, and (2) participants learned a dynamic full-body balance task.

Methods

24 participants (14 female, 10 male) participated in the balance training intervention, and 28 were matched controls (16 female, 12 male). Correlational tractography was used to analyze changes in WM from pre- to post-training.

Results

Both females and males demonstrated skill acquisition, yet there were significant differences in measures of WM between females and males. These data support a growing body of evidence suggesting that females exhibit increased WM neuroplasticity changes relative to males despite comparable changes in motor behavior (e.g., balance).

Discussion

The biological sex differences reported here may represent an important factor to consider in both basic research (e.g., collapsing across females and males) as well as future clinical studies of neuroplasticity associated with motor function (e.g., tailored rehabilitation approaches).

Theta burst stimulation on the fronto-cerebellar connective network promotes cognitive processing speed in the simple cognitive task

Background

The fronto-cerebellar functional network has been proposed to subserve cognitive processing speed. This study aims to elucidate how the long-range frontal-to-cerebellar effective connectivity contributes to faster speed.

Methods

In total, 60 healthy participants were randomly allocated to three five-daily sessions of transcranial magnetic stimulation conditions, namely intermittent theta-burst stimulation (iTBS, excitatory), continuous theta-burst stimulation (CTBS, inhibitory), or a sham condition. The sites of the stimulations were the right pre-supplementary motor area (RpSMA), medial cerebellar vermis VI (MCV6), and vertex, respectively. Performances in two reaction time tasks were recorded at different time points.

Results

Post-stimulation speeds revealed marginal decreases in the simple but not complex task. Nevertheless, participants in the excitatory RpSMA and inhibitory MCV6 conditions showed direct and negative path effects on faster speeds compared to the sham condition in the simple reaction time (SRT) task (β = -0.320, p = 0.045 and β = -0.414, p = 0.007, respectively). These path effects were not observed in the SDMT task.

Discussion

RpSMA and MCV6 were involved in promoting the path effects of faster reaction times on simple cognitive task. This study offers further evidence to support their roles within the long-range frontal-to-cerebellar connectivity subserving cognitive processing speed. The enhancement effects, however, are likely limited to simple rather than complex mental operations.

White matter functional gradients and their formation in adolescence

It is well known that functional magnetic resonance imaging (fMRI) is a widely used tool for studying brain activity. Recent research has shown that fluctuations in fMRI data can reflect functionally meaningful patterns of brain activity within the white matter. We leveraged resting-state fMRI from an adolescent population to characterize large-scale white matter functional gradients and their formation during adolescence. The white matter showed gray-matter-like unimodal-to-transmodal and sensorimotor-to-visual gradients with specific cognitive associations and a unique superficial-to-deep gradient with nonspecific cognitive associations. We propose two mechanisms for their formation in adolescence. One is a "function-molded" mechanism that may mediate the maturation of the transmodal white matter via the transmodal gray matter. The other is a "structure-root" mechanism that may support the mutual mediation roles of the unimodal and transmodal white matter maturation during adolescence. Thus, the spatial layout of the white matter functional gradients is in concert with the gray matter functional organization. The formation of the white matter functional gradients may be driven by brain anatomical wiring and functional needs.

Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI

Introduction

Current studies of structural covariance networks were focused on the gray matter in the human brain. The structural covariance connectivity in the white matter remains largely unexplored. This paper aimed to build novel metrics that can infer white matter structural covariance connectivity, and to explore the predictive power of the proposed features.

Methods

To this end, a cohort of 315 adult subjects with the anatomical brain MRI datasets were obtained from the publicly available Dallas Lifespan Brain Study (DLBS) project. The 3D wavelet transform was applied on the individual voxel-based morphology (VBM) volume to obtain the white matter structural covariance connectivity. The predictive models for cognitive functions were built using support vector regression (SVR).

Results

The predictive models exhibited comparable performance with previous studies. The novel features successfully predicted the individual ability of digit comparison (DC) (r = 0.41 ± 0.01, p < 0.01) and digit symbol (DSYM) (r = 0.5 ± 0.01, p < 0.01). The sensorimotor-related white matter system exhibited as the most predictive network node. Furthermore, the node strengths of sensorimotor mode were significantly correlated to cognitive scores.

Discussion

The results suggested that the white matter structural covariance connectivity was informative and had potential for predictive tasks of brain-behavior research.

Vascular Cognitive Impairment After Mild Stroke: Connectomic Insights, Neuroimaging, and Knowledge Translation

Contemporary stroke assessment protocols have a limited ability to detect vascular cognitive impairment (VCI), especially among those with subtle deficits. This lesser-involved categorization, termed mild stroke (MiS), can manifest compromised processing speed that negatively impacts cognition. From a neurorehabilitation perspective, research spanning neuroimaging, neuroinformatics, and cognitive neuroscience supports that processing speed is a valuable proxy for complex neurocognitive operations, insofar as inefficient neural network computation significantly affects daily task performance. This impact is particularly evident when high cognitive loads compromise network efficiency by challenging task speed, complexity, and duration. Screening for VCI using processing speed metrics can be more sensitive and specific. Further, they can inform rehabilitation approaches that enhance patient recovery, clarify the construct of MiS, support clinician-researcher symbiosis, and further clarify the occupational therapy role in targeting functional cognition. To this end, we review relationships between insult-derived connectome alterations and VCI, and discuss novel clinical approaches for identifying disruptions of neural networks and white matter connectivity. Furthermore, we will frame knowledge translation efforts to leverage insights from cutting-edge structural and functional connectomics research. Lastly, we highlight how occupational therapists can provide expertise as knowledge brokers acting within their established scope of practice to drive substantive clinical innovation.

Characteristics of disrupted topological organization in white matter functional connectome in schizophrenia

BACKGROUND

Neuroimaging characteristics have demonstrated disrupted functional organization in schizophrenia (SZ), involving large-scale networks within grey matter (GM). However, previous studies have ignored the role of white matter (WM) in supporting brain function.

METHODS

Using resting-state functional MRI and graph theoretical approaches, we investigated global topological disruptions of large-scale WM and GM networks in 93 SZ patients and 122 controls. Six global properties [clustering coefficient (Cp), shortest path length (Lp), local efficiency (Eloc), small-worldness (σ), hierarchy (β) and synchronization (S) and three nodal metrics [nodal degree (Knodal), nodal efficiency (Enodal) and nodal betweenness (Bnodal)] were utilized to quantify the topological organization in both WM and GM networks.

RESULTS

At the network level, both WM and GM networks exhibited reductions in Eloc, Cp and S in SZ. The SZ group showed reduced σ and β only for the WM network. Furthermore, the Cp, Eloc and S of the WM network were negatively correlated with negative symptoms in SZ. At the nodal level, the SZ showed nodal disturbances in the corpus callosum, optic radiation, posterior corona radiata and tempo-occipital WM tracts. For GM, the SZ manifested increased nodal centralities in frontoparietal regions and decreased nodal centralities in temporal regions.

CONCLUSIONS

These findings provide the first evidence for abnormal global topological properties in SZ from the perspective of a substantial whole brain, including GM and WM. Nodal centralities enhance GM areas, along with a reduction in adjacent WM, suggest that WM functional alterations may be compensated for adjacent GM impairments in SZ.

Acute Effects of a Perturbation-Based Balance Training on Cognitive Performance in Healthy Older Adults: A Pilot Study

Aging is accompanied by an alteration in the capacity to ambulate, react to external balance perturbations, and resolve cognitive tasks. Perturbation-based balance training has been used to induce adaptations of gait stability and reduce fall risk. The compensatory reactions generated in response to external perturbations depend on the activation of specific neural structures. This suggests that training balance recovery reactions should show acute cognitive training effects. This study aims to investigate whether exposure to repeated balance perturbations while walking can produce acute aftereffects that improve proactive and reactive strategies to control gait stability and cognitive performance in healthy older adults. It is expected that an adaptation of the recovery reactions would be associated with increased selective attention and information processing speed. Twenty-eight healthy older adults were assigned to either an Experimental (EG) or a Control Group (CG). The protocol was divided in 2 days. During the first visit, all participants completed the Symbol Digit Modalities Test (SDMT) and the Trail Making Test (TMT). During the second visit, a cable-driven robot was used to apply waist-pull perturbations while walking on a treadmill. The EG was trained with multidirectional perturbations of increasing intensity. The CG walked for a comparable amount of time with cables on, but without experiencing perturbations. Before and after the training, all participants were exposed to diagonal waist-pull perturbations. Changes in gait stability were evaluated by comparing the distance between the heel of the leading leg and the extrapolated Center of Mass (Heel-XCoM Distance-HXD) at perturbation onset (PON) and first compensatory heel strike (CHS). Finally, the cables were removed, and participants completed the SDMT and the TMT again. Results showed that only the EG adapted the gait stability (p < 0.001) in reaction to diagonal perturbations and showed improved performance in the SDMT (p < 0.001). This study provides the first evidence that a single session of perturbation-based balance training produce acute aftereffects in terms of increased cognitive performance and gait stability in healthy older adults. Future studies will include measures of functional activation of the cerebral cortex and examine whether a multi-session training will demonstrate chronic effects.

Function-structure coupling: White matter functional magnetic resonance imaging hyper-activation associates with structural integrity reductions in schizophrenia

White matter (WM) microstructure deficit may be an underlying factor in the brain dysconnectivity hypothesis of schizophrenia using diffusion tensor imaging (DTI). However, WM dysfunction is unclear in schizophrenia. This study aimed to investigate the association between structural deficits and functional disturbances in major WM tracts in schizophrenia. Using functional magnetic resonance imaging (fMRI) and DTI, we developed the skeleton-based WM functional analysis, which could achieve voxel-wise function-structure coupling by projecting the fMRI signals onto a skeleton in WM. We measured the fractional anisotropy (FA) and WM low-frequency oscillation (LFO) and their couplings in 93 schizophrenia patients and 122 healthy controls (HCs). An independent open database (62 schizophrenia patients and 71 HCs) was used to test the reproducibility. Finally, associations between WM LFO and five behaviour assessment categories (cognition, emotion, motor, personality and sensory) were examined. This study revealed a reversed pattern of structure and function in frontotemporal tracts, as follows. (a) WM hyper-LFO was associated with reduced FA in schizophrenia. (b) The function-structure association was positive in HCs but negative in schizophrenia patients. Furthermore, function-structure dissociation was exacerbated by long illness duration and severe negative symptoms. (c) WM activations were significantly related to cognition and emotion. This study indicated function-structure dys-coupling, with higher LFO and reduced structural integration in frontotemporal WM, which may reflect a potential mechanism in WM neuropathologic processing of schizophrenia.

Connectome-based models can predict processing speed in older adults

Decrement in processing speed (PS) is a primary cognitive morbidity in clinical populations and could significantly influence other cognitive functions, such as attention and memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translational implications on clinical and aging research. In this study, we verified that resting-state functional connectivity could be used to predict PS in 99 older adults by using connectome-based predictive modeling (CPM). We identified two distinct connectome patterns across the whole brain: the fast-PS and slow-PS networks. Relative to the slow-PS network, the fast-PS network showed more within-network connectivity in the motor and visual networks and less between-network connectivity in the motor-visual, motor-subcortical/cerebellum and motor-frontoparietal networks. We further verified that the connectivity patterns for prediction of PS were also useful for predicting attention and memory in the same sample. To test the generalizability and specificity of the connectome-based predictive models, we applied these two connectome models to an independent sample of three age groups (101 younger adults, 103 middle-aged adults and 91 older adults) and confirmed these models could specifically be generalized to predict PS of the older adults, but not the younger and middle-aged adults. Taking all the findings together, the identified connectome-based predictive models are strong for predicting PS in older adults. The application of CPM to predict neurocognitive abilities can complement conventional neurocognitive assessments, bring significant clinical benefits to patient management and aid the clinical diagnoses, prognoses and management of people undergoing the aging process.

White Matter Neuroplasticity: Motor Learning Activates the Internal Capsule and Reduces Hemodynamic Response Variability

Numerous studies have noted the importance of white matter changes in motor learning, but existing literature only focuses on structural and microstructural MRI changes, as there are limited tools available for in vivo investigations of white matter function. One method that has gained recent prominence is the application of blood oxygen level dependent (BOLD) fMRI to white matter, with high-field scanners now being able to better detect the smaller hemodynamic changes present in this tissue type compared to those in the gray matter. However, fMRI techniques have yet to be applied to investigations of neuroplastic change with motor learning in white matter. White matter function represents an unexplored component of neuroplasticity and is essential for gaining a complete understanding of learning-based changes occurring throughout the whole brain. Twelve healthy, right-handed participants completed fine motor and gross motor tasks with both hands, using an MRI compatible computer mouse. Using a crossover design along with a prior analysis approach to establish WM activation, participants received a baseline scan followed by 2 weeks of training, returning for a midpoint and endpoint scan. The motor tasks were designed to be selectively difficult for the left hand, leading to a training effect only in that condition. Analysis targeted the comparison and detection of training-associated right vs left hand changes. A statistically significant improvement in motor task score was only noted for the left-hand motor condition. A corresponding change in the temporal characteristics of the white matter hemodynamic response was shown within only the right corticospinal tract. The hemodynamic response exhibited a reduction in the dispersion characteristics after the training period. To our knowledge, this is the first report of MRI detectable functional neuroplasticity in white matter, suggesting that modifications in temporal characteristics of white matter hemodynamics may underlie functional neuroplasticity in this tissue.

A neuromarker of individual general fluid intelligence from the white-matter functional connectome

Neuroimaging studies have uncovered the neural roots of individual differences in human general fluid intelligence (Gf). Gf is characterized by the function of specific neural circuits in brain gray-matter; however, the association between Gf and neural function in brain white-matter (WM) remains unclear. Given reliable detection of blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) signals in WM, we used a functional, rather than an anatomical, neuromarker in WM to identify individual Gf. We collected longitudinal BOLD-fMRI data (in total three times, ~11 months between time 1 and time 2, and ~29 months between time 1 and time 3) in normal volunteers at rest, and identified WM functional connectomes that predicted the individual Gf at time 1 (n = 326). From internal validation analyses, we demonstrated that the constructed predictive model at time 1 predicted an individual's Gf from WM functional connectomes at time 2 (time 1 ∩ time 2: n = 105) and further at time 3 (time 1 ∩ time 3: n = 83). From external validation analyses, we demonstrated that the predictive model from time 1 was generalized to unseen individuals from another center (n = 53). From anatomical aspects, WM functional connectivity showing high predictive power predominantly included the superior longitudinal fasciculus system, deep frontal WM, and ventral frontoparietal tracts. These results thus demonstrated that WM functional connectomes offer a novel applicable neuromarker of Gf and supplement the gray-matter connectomes to explore brain-behavior relationships.

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.

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.

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.

Robust Motion Regression of Resting-State Data Using a Convolutional Neural Network Model

Resting-state functional magnetic resonance imaging (rs-fMRI) based on the blood-oxygen-level-dependent (BOLD) signal has been widely used in healthy individuals and patients to investigate brain functions when the subjects are in a resting or task-negative state. Head motion considerably confounds the interpretation of rs-fMRI data. Nuisance regression is commonly used to reduce motion-related artifacts with six motion parameters estimated from rigid-body realignment as regressors. To further compensate for the effect of head movement, the first-order temporal derivatives of motion parameters and squared motion parameters were proposed previously as possible motion regressors. However, these additional regressors may not be sufficient to model the impact of head motion because of the complexity of motion artifacts. In addition, while using more motion-related regressors could explain more variance in the data, the neural signal may also be removed with increasing number of motion regressors. To better model how in-scanner motion affects rs-fMRI data, a robust and automated convolutional neural network (CNN) model is developed in this study to obtain optimal motion regressors. The CNN network consists of two temporal convolutional layers and the output from the network are the derived motion regressors used in the following nuisance regression. The temporal convolutional layer in the network can non-parametrically model the prolonged effect of head motion. The set of regressors derived from the neural network is compared with the same number of regressors used in a traditional nuisance regression approach. It is demonstrated that the CNN-derived regressors can more effectively reduce motion-related artifacts.

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.

Symbol Digit Modalities Test adaptation for Magnetic Resonance Imaging environment: A systematic review and meta-analysis

BACKGROUND

The Symbol Digit Modalities Test (SDMT) is widely used for cognitive evaluation of information processing speed (IPS), required in many cognitive operations. Despite being unspecific for different neurological disorders, it is sensitive to assess impaired performance related to stroke, Parkinson's disease, traumatic brain injury, and multiple sclerosis. However, in addition to evaluate the presence and severity of IPS impairment, it is of interest to determine the localization and integration of brain regions responsible for the functions assessed by the SDMT.

OBJECTIVE

To review the studies that adapted the SDMT to the magnetic resonance environment and obtain the brain areas associated with the performance of the task in healthy subjects with a meta-analysis.

METHODOLOGY

A systematic review was performed using ten studies published between 1990 and 2017, and selected from four databases. All studies included participants of both genders and age between 18 and 50 years, used Functional Magnetic Resonance Imaging (fMRI) and SDMT adaptation and reported brain regions associated with the task. Six of them also reported the region coordinates in a standard space, so they were included in a meta-analysis. Activation Likelihood Estimation algorithm, with significance for p < 0.05 corrected for multiple comparisons, was used to identify areas that are robustly related to the performance of the SDMT.

RESULTS

The areas predominantly reported in the studies of our meta-analysis were regions of the frontoparietal attentional network and occipital cortex, as well as cuneus, precuneus, and cerebellum. Individually all regions that survived the statistical threshold are consistent with what is expected after reviewing prospective studies.

CONCLUSIONS

The present study allowed the identification of brain areas activated during the performance of the SDMT in healthy subjects, and therefore it will help understanding the differences in brain activation by this task in clinical populations. Moreover, it may guide future studies of therapeutic strategies and interventions in those populations.

Evaluation of cortical local field potential diffusion in stereotactic electro-encephalography recordings: A glimpse on white matter signal

While there is a strong interest in meso-scale field potential recording using intracranial electroencephalography with penetrating depth electrodes (i.e. stereotactic EEG or S-EEG) in humans, the signal recorded in the white matter remains ignored. White matter is generally considered electrically neutral and often included in the reference montage. Moreover, re-referencing electrophysiological data is a critical preprocessing choice that could drastically impact signal content and consequently the results of any given analysis. In the present stereotactic electroencephalography study, we first illustrate empirically the consequences of commonly used references (subdermal, white matter, global average, local montage) on inter-electrode signal correlation. Since most of these reference montages incorporate white matter signal, we next consider the difference between signals recorded in cortical gray matter and white matter. Our results reveal that electrode contacts located in the white matter record a mixture of activity, with part arising from the volume conduction (zero time delay) of activity from nearby gray matter. Furthermore, our analysis shows that white matter signal may be correlated with distant gray matter signal. While residual passive electrical spread from nearby matter may account for this relationship, our results suggest the possibility that this long distance correlation arises from the white matter fiber tracts themselves (i.e. activity from distant gray matter traveling along axonal fibers with time lag larger than zero); yet definitive conclusions about the origin of the white matter signal would require further experimental substantiation. By characterizing the properties of signals recorded in white matter and in gray matter, this study illustrates the importance of including anatomical prior knowledge when analyzing S-EEG data.

Visualizing functional pathways in the human brain using correlation tensors and magnetic resonance imaging

Functional magnetic resonance imaging usually detects changes in blood oxygenation level dependent (BOLD) signals from T2*-sensitive acquisitions, and is most effective in detecting activity in brain cortex which is irrigated by rich vasculature to meet high metabolic demands. We recently demonstrated that MRI signals from T2*-sensitive acquisitions in a resting state exhibit structure-specific temporal correlations along white matter tracts. In this report we validate our preliminary findings and introduce spatio-temporal functional correlation tensors to characterize the directional preferences of temporal correlations in MRI signals acquired at rest. The results bear a remarkable similarity to data obtained by diffusion tensor imaging but without any diffusion-encoding gradients. Just as in gray matter, temporal correlations in resting state signals may reflect intrinsic synchronizations of neural activity in white matter. Here we demonstrate that functional correlation tensors are able to visualize long range white matter tracts as well as short range sub-cortical fibers imaged at rest, and that evoked functional activities alter these structures and enhance the visualization of relevant neural circuitry. Furthermore, we explore the biophysical mechanisms underlying these phenomena by comparing pulse sequences, which suggest that white matter signal variations are consistent with hemodynamic (BOLD) changes associated with neural activity. These results suggest new ways to evaluate MRI signal changes within white matter.

Postoperative Cognitive Dysfunction: Minding the Gaps in Our Knowledge of a Common Postoperative Complication in the Elderly

Postoperative cognitive dysfunction (POCD) is a common complication associated with significant morbidity and mortality in elderly patients. There is much interest in and controversy about POCD, reflected partly in the increasing number of articles published on POCD recently. Recent work suggests surgery may also be associated with cognitive improvement in some patients, termed postoperative cognitive improvement (POCI). As the number of surgeries performed worldwide approaches 250 million per year, optimizing postoperative cognitive function and preventing/treating POCD are major public health issues. In this article, we review the literature on POCD and POCI, and discuss current research challenges in this area.

Abnormal White Matter Blood-Oxygen-Level-Dependent Signals in Chronic Mild Traumatic Brain Injury

Concussion, or mild traumatic brain injury (mTBI), can cause persistent behavioral symptoms and cognitive impairment, but it is unclear if this condition is associated with detectable structural or functional brain changes. At two sites, chronic mTBI human subjects with persistent post-concussive symptoms (three months to five years after injury) and age- and education-matched healthy human control subjects underwent extensive neuropsychological and visual tracking eye movement tests. At one site, patients and controls also performed the visual tracking tasks while blood-oxygen-level-dependent (BOLD) signals were measured with functional magnetic resonance imaging. Although neither neuropsychological nor visual tracking measures distinguished patients from controls at the level of individual subjects, abnormal BOLD signals were reliably detected in patients. The most consistent changes were localized in white matter regions: anterior internal capsule and superior longitudinal fasciculus. In contrast, BOLD signals were normal in cortical regions, such as the frontal eye field and intraparietal sulcus, that mediate oculomotor and attention functions necessary for visual tracking. The abnormal BOLD signals accurately differentiated chronic mTBI patients from healthy controls at the single-subject level, although they did not correlate with symptoms or neuropsychological performance. We conclude that subjects with persistent post-concussive symptoms can be identified years after their TBI using fMRI and an eye movement task despite showing normal structural MRI and DTI.

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

Functional magnetic resonance imaging (fMRI) is a non-invasive technique that allows for visualization of activated brain regions. Until recently, fMRI studies have focused on gray matter. There are two main reasons white matter fMRI remains controversial: (1) the blood oxygen level dependent (BOLD) fMRI signal depends on cerebral blood flow and volume, which are lower in white matter than gray matter and (2) fMRI signal has been associated with post-synaptic potentials (mainly localized in gray matter) as opposed to action potentials (the primary type of neural activity in white matter). Despite these observations, there is no direct evidence against measuring fMRI activation in white matter and reports of fMRI activation in white matter continue to increase. The questions underlying white matter fMRI activation are important. White matter fMRI activation has the potential to greatly expand the breadth of brain connectivity research, as well as improve the assessment and diagnosis of white matter and connectivity disorders. The current review provides an overview of the motivation to investigate white matter fMRI activation, as well as the published evidence of this phenomenon. We speculate on possible neurophysiologic bases of white matter fMRI signals, and discuss potential explanations for why reports of white matter fMRI activation are relatively scarce. We end with a discussion of future basic and clinical research directions in the study of white matter fMRI.