Functional brain network changes associated with maintenance of cognitive function in multiple sclerosis

Symmetry differences of structural connectivity in multiple sclerosis and healthy state

Focal and diffuse cerebral damages occur in Multiple Sclerosis (MS) that promotes profound shifts in local and global structural connectivity parameters, mainly derived from diffusion tensor imaging. Most of the reconstruction analyses have applied conventional tracking algorithms largely based on the controversial streamline count. For a more credible explanation of the diffusion MRI signal, we used convex optimization modeling for the microstructure-informed tractography2 (COMMIT2) framework. All multi-shell diffusion data from 40 healthy controls (HCs) and 40 relapsing-remitting MS (RRMS) patients were transformed into COMMIT2-weighted matrices based on the Schefer-200 parcels atlas (7 networks) and 14 bilateral subcortical regions. The success of the classification process between MS and healthy state was efficiently predicted by the left DMN-related structures and visual network-associated pathways. Additionally, the lesion volume and age of onset were remarkably correlated with the components of the left DMN. Using complementary approaches such as global metrics revealed differences in WM microstructural integrity between MS and HCs (efficiency, strength). Our findings demonstrated that the cutting-edge diffusion MRI biomarkers could hold the potential for interpreting brain abnormalities in a more distinctive way.

Multistage classification identifies altered cortical phase- and amplitude-coupling in Multiple Sclerosis

Distinguishing groups of subjects or experimental conditions in a high-dimensional feature space is a common goal in modern neuroimaging studies. Successful classification depends on the selection of relevant features as not every neuronal signal component or parameter is informative about the research question at hand. Here, we developed a novel unsupervised multistage analysis approach that combines dimensionality reduction, bootstrap aggregating and multivariate classification to select relevant neuronal features. We tested the approach by identifying changes of brain-wide electrophysiological coupling in Multiple Sclerosis. Multiple Sclerosis is a demyelinating disease of the central nervous system that can result in cognitive decline and physical disability. However, related changes in large-scale brain interactions remain poorly understood and corresponding non-invasive biomarkers are sparse. We thus compared brain-wide phase- and amplitude-coupling of frequency specific neuronal activity in relapsing-remitting Multiple Sclerosis patients (n = 17) and healthy controls (n = 17) using magnetoencephalography. Changes in this dataset included both, increased and decreased phase- and amplitude-coupling in wide-spread, bilateral neuronal networks across a broad range of frequencies. These changes allowed to successfully classify patients and controls with an accuracy of 84%. Furthermore, classification confidence predicted behavioral scores of disease severity. In sum, our results unravel systematic changes of large-scale phase- and amplitude coupling in Multiple Sclerosis. Furthermore, our results establish a new analysis approach to efficiently contrast high-dimensional neuroimaging data between experimental groups or conditions.

Functional Connectivity Lateralisation Shift of Resting State Networks is Linked to Visuospatial Memory and White Matter Microstructure in Relapsing-Remitting Multiple Sclerosis

Laterality patterns of resting state networks (RSN) change in various neuropsychiatric conditions. Multiple sclerosis (MS) causes neuro-cognitive symptoms involving dysfunctional large-scale brain networks. Yet, whether healthy laterality patterns of RSNs are maintained in MS and whether altered laterality patterns explain disease symptoms has not been explicitly investigated. We analysed functional MRI and diffusion tensor imaging data from 24 relapsing–remitting MS patients and 25 healthy participants. We performed group-level independent component analysis and used dual regression to estimate individual versions of well-established RSNs. Voxelwise laterality indices were calculated for each RSN. Group differences were assessed via a general linear model-based approach. The relationship between functional laterality and white matter microstructural asymmetry was assessed using Tract-Based Spatial Statistics. Spearman’s correlation was calculated between laterality indices and Brief International Cognitive Assessment for Multiple Sclerosis scores. Functional laterality of the dorsal attention network showed a significant leftward shift in the MS group in the posterior intraparietal sulcus (p < 0.033). Default-mode network laterality showed a significant leftward shift in the MS group in the angular gyrus (p < 0.005). Diminished dorsal attention network laterality was associated with increased fractional anisotropy asymmetry in the superior longitudinal fasciculus (p < 0.02). In the default-mode network, leftward laterality of the angular gyrus was associated with higher BVMT-R scores (R = − 0.52, p < 0.023). Our results confirm previous descriptions of RSN dysfunction in relapsing–remitting MS and show that altered functional connectivity lateralisation patterns of RSNs might contibute to cognitive performance and structural remodellation even in patients with mild clinical symptoms.

Functional brain network organization measured with magnetoencephalography predicts cognitive decline in multiple sclerosis

BACKGROUND

Cognitive decline remains difficult to predict as structural brain damage cannot fully explain the extensive heterogeneity found between MS patients.

OBJECTIVE

To investigate whether functional brain network organization measured with magnetoencephalography (MEG) predicts cognitive decline in MS patients after 5 years and to explore its value beyond structural pathology.

METHODS

Resting-state MEG recordings, structural MRI, and neuropsychological assessments were analyzed of 146 MS patients, and 100 patients had a 5-year follow-up neuropsychological assessment. Network properties of the minimum spanning tree (i.e. backbone of the functional brain network) indicating network integration and overload were related to baseline and longitudinal cognition, correcting for structural damage.

RESULTS

A more integrated beta band network (i.e. smaller diameter) and a less integrated delta band network (i.e. lower leaf fraction) predicted cognitive decline after 5 years (Radj2=15%), independent of structural damage. Cross-sectional analyses showed that a less integrated network (e.g. lower tree hierarchy) related to worse cognition, independent of frequency band.

CONCLUSIONS

The level of functional brain network integration was an independent predictive marker of cognitive decline, in addition to the severity of structural damage. This work thereby indicates the promise of MEG-derived network measures in predicting disease progression in MS.

Altered brain network function during attention-modulated visual processing in multiple sclerosis

BACKGROUND

Multiple sclerosis may damage cognitive performance in several domains, including attention. Although attention network deficits were described during rest, studies that investigate their function during task performance are scarce.

OBJECTIVE

To investigate connectivity within and between task-related networks in multiple sclerosis during a visual attention task as a function of cognitive performance.

METHODS

A total of 23 relapsing-remitting multiple sclerosis (RRMS) patients and 29 healthy controls underwent task-functional magnetic resonance imaging (fMRI) scans using a visual attention paradigm on a 3T scanner. Scans were analysed using tensor-independent component analysis (TICA). Functional connectivity was calculated within and between components. We assessed cognitive function with the Brief International Cognitive Assessment for MS (BICAMS) battery.

RESULTS

TICA extracted components related to visual processing, attention, executive function and the default-mode network. Subject scores of visual/attention-related and executive components were greater in healthy controls (p < 0.032, p < 0.023). Connectivity between visual/attention-related and default-mode components was higher in patients (p < 0.043), correlating with Brief Visuospatial Memory Test-Revised (BVMT-R) scores (R = -0.48, p < 0.036). Patients showed reduced connectivity between the right intraparietal sulcus (rIPS) and frontal eye field (rFEF), and bilateral frontal eye fields (p < 0.012, p < 0.003). Reduced rIPS-rFEF connectivity came with lower Symbol Digit Modalities Test (SDMT)/BVMT-R scores in patients (R = 0.53, p < 0.02, R = 0.46, p < 0.049).

CONCLUSION

Attention-related networks show altered connectivity during task performance in RRMS patients, scaling with cognitive disability.

Mechanisms of neurobehavioral abnormalities in multiple sclerosis: Contributions from neural and immune components

Multiple sclerosis-related neurobehavioral abnormalities are one of the main components of disability in this disease. The same pathological processes that explain demyelination periods and neurodegeneration also allow the comprehension of neurobehavioral abnormalities. Inflammation in the central nervous system caused by cells of the immune system, especially lymphocytes, and by resident cells, such as astrocytes and microglia, directly modulate neurotransmission and synaptic physiology, resulting in behavioral changes (such as sickness behavior) and amplifying the degenerative mechanisms that occur in multiple sclerosis. In addition, neuronal death caused by glutamate-mediated excitotoxicity, alterations in GABAergic, serotonergic, and dopaminergic neurotransmission, and the mechanisms of axon damage are of foremost importance to explain the reduction in brain volume and the associated cognitive decline. Neuroinflammation and neurodegeneration are not isolated phenomena and various instances of interaction between them have been described. This presents attractive targets for the development of therapeutic strategies for this neglected component of multiple sclerosis related disability.

The Role of fMRI in the Assessment of Neuroplasticity in MS: A Systematic Review

Neuroplasticity, which is the ability of the brain to adapt to internal and external environmental changes, physiologically occurs during growth and in response to damage. The brain's response to damage is of particular interest in multiple sclerosis, a chronic disease characterized by inflammatory and neurodegenerative damage to the central nervous system. Functional MRI (fMRI) is a tool that allows functional changes related to the disease and to its evolution to be studied in vivo. Several studies have shown that abnormal brain recruitment during the execution of a task starts in the early phases of multiple sclerosis. The increased functional activation during a specific task observed has been interpreted mainly as a mechanism of adaptive plasticity designed to contrast the increase in tissue damage. More recent fMRI studies, which have focused on the activity of brain regions at rest, have yielded nonunivocal results, suggesting that changes in functional brain connections represent mechanisms of either adaptive or maladaptive plasticity. The few longitudinal studies available to date on disease evolution have also yielded discrepant results that are likely to depend on the clinical features considered and the length of the follow-up. Lastly, fMRI has been used in interventional studies to investigate plastic changes induced by pharmacological therapy or rehabilitation, though whether such changes represent a surrogate of neuroplasticity remains unclear. The aim of this paper is to systematically review the existing literature in order to provide an overall description of both the neuroplastic process itself and the evolution in the use of fMRI techniques as a means of assessing neuroplasticity. The quantitative and qualitative approach adopted here ensures an objective analysis of published, peer-reviewed research and yields an overview of up-to-date knowledge.

The role of optical coherence tomography and infrared oculography in assessing the visual pathway and CNS in multiple sclerosis

In this review, a current overview is provided of how optical coherence tomography and infrared oculography can aid in assessing the visual system and CNS in multiple sclerosis (MS). Both afferent and efferent visual disorders are common in MS and visual complaints can have a tremendous impact on daily functioning. Optical coherence tomography and infrared oculography can detect and quantify visual disorders with high accuracy, but could also serve as quantitative markers for inflammation, neurodegeneration and network changes including cognitive decline in MS patients. The assessment of the efferent and afferent visual pathways is relevant for monitoring and predicting the disease course, but is also potentially valuable as an outcome measure in therapeutic trials.

Altered motor plasticity in an acute relapse of multiple sclerosis

In relapsing-remitting MS (RRMS), the symptoms of a clinical relapse subside over time. Neuroplasticity is believed to play an important compensatory role. In this study, we assessed excitability-decreasing plasticity during an acute relapse of MS and 12 weeks afterwards. Motor plasticity was examined in 19 patients with clinically isolated syndrome or RRMS during a steroid-treated relapse (t1) and 12 weeks afterwards (t2) using paired-associative stimulation (PAS10). This method combines repetitive electric nerve stimulation with transcranial magnetic stimulation of the contralateral motor cortex to model long-term synaptic depression in the human cortex. Additionally, 19 age-matched healthy controls were assessed. Motor-evoked potentials of the abductor pollicis brevis muscle were recorded before and after intervention. Clinical disability was assessed by the multiple sclerosis functional composite and the subscore of the nine-hole peg test taken as a measure of hand function. The effect of PAS10 was significantly different between controls and patients; at t1, but not at t2, baseline-normalized postinterventional amplitudes were significantly higher in patients (106 [IQR 98-137] % post10-15 and 111 [IQR 88-133] % post20-25) compared to controls (92 [IQR 85-111] % and 90 [IQR 75-102] %). Additional exploratory analysis indicated a potentially excitability-enhancing effect of PAS10 in patients as opposed to controls. Significant clinical improvement between t1 and t2 was not correlated with PAS10 effects. Our results indicate an alteration of PAS10-induced synaptic plasticity during relapse, presumably reflecting a polarity shift due to metaplastic processes within the motor cortex. Further studies will need to elucidate the functional significance of such changes for the clinical course of MS.

Cognitive Impairment in Multiple Sclerosis: A Review of Neuropsychological Assessments

Of the more than two million people worldwide with multiple sclerosis, 40% to 65% experience cognitive impairment, many of them early in the course of the disease. Cognitive impairment has been found in patients with all subtypes of multiple sclerosis. Because both pharmacologic and nonpharmacologic interventions may improve patients' brain function, cognitive assessment should be a routine part of the clinical evaluation. Traditional paper-and-pencil neuropsychological tests and batteries can help detect and monitor patients' cognitive problems. Computerized cognitive batteries also show promise. Controversy continues over which test is most reliable at assessing cognitive impairment in both everyday clinical practice and research. Each battery has possible disadvantages, such as practice effects, poor sensitivity and specificity, and questionable applicability to multiple sclerosis. Based on our review of the literature, we describe the tests that are currently being used or that might be used in assessing cognitive deficits in patients with multiple sclerosis, and we summarize the strengths and limitations of each.

Application of diffusional kurtosis imaging to detect occult brain damage in multiple sclerosis and neuromyelitis optica

Multiple sclerosis (MS) and neuromyelitis optica (NMO) are two common types of inflammatory demyelinating disease of the central nervous system. Early distinction of NMO from MS is crucial but quite challenging. In this study, 13 NMO spectrum disorder patients (Expanded Disability Status Scale (EDSS) of 3.0 ± 1.7, ranging from 2 to 6.5; disease duration of 5.3 ± 4.7 years), 17 relapsing-remitting MS patients (EDSS of 2.6 ± 1.4, ranging from 1 to 5.5; disease duration of 7.9 ± 7.8 years) and 18 healthy volunteers were recruited. Diffusional kurtosis imaging was employed to discriminate NMO and MS patients at the early or stable stage from each other, and from healthy volunteers. The presence of alterations in diffusion and diffusional kurtosis metrics in normal-appearing white matter (NAWM) and diffusely increased mean diffusivity (MD) in the cortical normal-appearing gray matter (NAGM) favors the diagnosis of MS rather than NMO. Meanwhile, normal diffusivities and kurtosis metrics in all NAWM as well as increases in MD in the frontal and temporal NAGM suggest NMO. Our results suggest that diffusion and diffusional kurtosis metrics may well aid in discriminating the two diseases.

Fluctuations of spontaneous EEG topographies predict disease state in relapsing-remitting multiple sclerosis

Spontaneous fluctuations of neuronal activity in large-scale distributed networks are a hallmark of the resting brain. In relapsing-remitting multiple sclerosis (RRMS) several fMRI studies have suggested altered resting-state connectivity patterns. Topographical EEG analysis reveals much faster temporal fluctuations in the tens of milliseconds time range (termed “microstates”), which showed altered properties in a number of neuropsychiatric conditions.

We investigated whether these microstates were altered in patients with RRMS, and if the microstates' temporal properties reflected a link to the patients' clinical features.

We acquired 256-channel EEG in 53 patients (mean age 37.6 years, 45 females, mean disease duration 9.99 years, Expanded Disability Status Scale ≤ 4, mean 2.2) and 49 healthy controls (mean age 36.4 years, 33 females). We analyzed segments of a total of 5 min of EEG during resting wakefulness and determined for both groups the four predominant microstates using established clustering methods.

We found significant differences in the temporal dynamics of two of the four microstates between healthy controls and patients with RRMS in terms of increased appearance and prolonged duration. Using stepwise multiple linear regression models with 8-fold cross-validation, we found evidence that these electrophysiological measures predicted a patient's total disease duration, annual relapse rate, disability score, as well as depression score, and cognitive fatigue measure.

In RRMS patients, microstate analysis captured altered fluctuations of EEG topographies in the sub-second range. This measure of high temporal resolution provided potentially powerful markers of disease activity and neuropsychiatric co-morbidities in RRMS.

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EEG microstates analyses provide high resolution of temporal dynamics of brain networks.

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Temporal parameters of EEG microstates are altered in Multiple Sclerosis

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Altered microstate parameters predict several clinical characteristics in patients

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We propose an EEG microstate based marker to characterize disease evolution in patients

Music Listening modulates Functional Connectivity and Information Flow in the Human Brain

Listening to familiar music has recently been reported to be beneficial during recovery from stroke. A better understanding of changes in functional connectivity and information flow is warranted in order to further optimize and target this approach through music therapy. Twelve healthy volunteers listened to seven different auditory samples during an fMRI scanning session: a musical piece chosen by the volunteer that evokes a strong emotional response (referred to as: "self-selected emotional"), two unfamiliar music pieces (Invention #1 by J. S. Bach* and Gagaku - Japanese classical opera, referred to as "unfamiliar"), the Bach piece repeated with visual guidance (DML: Directed Music Listening) and three spoken language pieces (unfamiliar African click language, an excerpt of emotionally charged language, and an unemotional reading of a news bulletin). Functional connectivity and betweenness (BTW) maps, a measure for information flow, were created with a graph-theoretical approach. Distinct variation in functional connectivity was found for different music pieces consistently for all subjects. Largest brain areas were recruited for processing self-selected music with emotional attachment or culturally unfamiliar music. Maps of information flow correlated significantly with fMRI BOLD activation maps (p<0.05). Observed differences in BOLD activation and functional connectivity may help explain previously observed beneficial effects in stroke recovery, as increased blood flow to damaged brain areas stimulated by active engagement through music listening may have supported a state more conducive to therapy.

Walking impairs cognitive performance among people with multiple sclerosis but not controls

People with multiple sclerosis (MS) complain of problems completing two tasks simultaneously; sometimes called 'dual-tasking' (DT). Previous research in DT among people with MS has focused on how adding a cognitive task interferes with gait and few have measured how adding a motor task could interfere with cognition. We aimed to determine the extent to which walking affects a concurrent working memory task in people with MS compared to healthy controls. We recruited MS participants (n=13) and controls (n=10) matched by age (±3years), education (±3years) and gender. Participants first completed the cognitive task (subtracting 7's from the previous number) and then again while walking on an instrumented walkway. Although there were no baseline differences in cognition or walking between MS participants and controls, MS participants demonstrated a 52% decrease in number of correct answers during DT (p<0.001). Mental Tracking Rate (% correct answers/min) correlated strongly with MS-related disability measured using the Expanded Disability Status Scale (EDSS; r(11)=-0.68, p<0.01). We propose that compromised mental tracking during walking could be related to limited neural resource capacity and could be a potentially useful outcome measure to detect ecologically valid dual tasking impairments.

The sleepy teenager - diagnostic challenges

The sleepy teenager puts the doctor in a, often tricky, situation where it must be decided if we deal with normal physiology or if we should suspect pathological conditions. What medical investigations are proper to consider? What differential diagnoses should be considered in the first place? And what tools do we actually have? The symptoms and problems that usually are presented at the clinical visit can be both of medical and psychosocial character – and actually they are often a mixture of both. Subsequently, the challenge to investigate the sleepy teenager often includes the examination of a complex behavioral pattern. It is important to train and develop diagnostic skills and to realize that the physiological or pathological conditions that can cause the symptoms may have different explanations. Research in sleep disorders has shown different pathological mechanisms congruent with the variations in the clinical picture. There are probably also different patterns of involved neuronal circuits although common pathways may exist. The whole picture remains to be drawn in this interesting and challenging area.

Thalamo-striato-cortical determinants to fatigue in multiple sclerosis

BACKGROUND

The aim was to explore the thalamo-striato-cortical theory of central fatigue in multiple sclerosis (MS) patients with self-reported fatigue. If the theory correctly predicted fatigue based on disruptions of the thalamo-striato-cortical network, we expected altered brain activation in this network in MS participants while performing a complex cognitive task that challenged fatigue.

METHODS

MS participants with self-reported fatigue were examined by functional magnetic resonance imaging (fMRI) during the performance of a complex working memory task. In this task, cognitive effort was challenged by a parametric design, which modeled the cerebral responses at increasing cognitive demands. In order to explore the theory of central fatigue in MS we also analyzed the cerebral responses by adding perceived fatigue scores as covariates in the analysis and by calculating the functional connectivity between regions in the thalamo-striatocortical network. The main findings were that MS participants elicited altered brain responses in the thalamo-striato-cortical network, and that brain activation in the left posterior parietal cortex and the right substantia nigra was positively correlated to perceived fatigue ratings. MS participants had stronger cortical-to-cortical and subcortical-to-subcortical connections, whereas they had weaker cortical-to-subcortical connections.

CONCLUSIONS

The findings of the present study indicate that the thalamo-striato-cortical network is involved in the pathophysiology of fatigue in MS, and provide support for the theory of central fatigue. However, due to the limited number of participants and the somewhat heterogeneous sample of MS participants, these results have to be regarded as tentative, though they might serve as a basis for future studies.

Abnormalities of frontal-parietal resting-state functional connectivity are related to disease activity in patients with systemic lupus erythematosus

Cerebral involvement is common in patients with systemic Lupus erythematosus (SLE) and is characterized by multiple clinical presentations, including cognitive disorders, headaches, and syncope. Several neuroimaging studies have demonstrated cerebral dysfunction during different tasks among SLE patients; however, there have been few studies designed to characterize network alterations or to identify clinical markers capable of reflecting the cerebral involvement in SLE patients. This study was designed to characterize the profile of the cerebral activation area and the functional connectivity of cognitive function in SLE patients by using a task-based and a resting state functional magnetic resonance imaging (fMRI) technique, and to determine whether or not any clinical biomarkers could serve as an indicator of cerebral involvement in this disease. The well-established cognitive function test (Paced Visual Serial Adding Test [PVSAT]) was used. Thirty SLE patients without neuropsychiatric symptoms and 25 age- and gender-matched healthy controls were examined using PVSAT task-based and resting state fMRI. Outside the scanner, the performance of patients and the healthy controls was similar. In the PVSAT task-based fMRI, patients presented significantly expanded areas of activation, and the activated areas exhibited significantly higher functional connectivity strength in patients in the resting state. A positive correlation existed between individual connectivity strength and disease activity scoring. No correlation with cerebral involvement existed for serum markers, such as C3, C4, and anti-dsDNA. Thus, our findings may shed new light on the pathologic mechanism underlying neuropsychiatric SLE, and suggests that disease activity may be a potential effective biomarker reflecting cerebral involvement in SLE.

Cognitive and clinical dysfunction, altered MEG resting-state networks and thalamic atrophy in multiple sclerosis

The relation between pathological findings and clinical and cognitive decline in Multiple Sclerosis remains unclear. Here, we tested the hypothesis that altered functional connectivity could provide a missing link between structural findings, such as thalamic atrophy and white matter lesion load, and clinical and cognitive dysfunction. Resting-state magnetoencephalography recordings from 21 MS patients and 17 gender- and age matched controls were projected onto atlas-based regions-of-interest using beamforming. Average functional connectivity was computed for each ROI and literature-based resting-state networks using the phase-lag index. Structural measures of whole brain and thalamic atrophy and lesion load were estimated from MRI scans. Global analyses showed lower functional connectivity in the alpha2 band and higher functional connectivity in the beta band in patients with Multiple Sclerosis. Additionally, alpha2 band functional connectivity was lower for the patients in two resting-state networks, namely the default mode network and the visual network. Higher beta band functional connectivity was found in the default mode network and in the temporo-parietal network. Lower alpha2 band functional connectivity in the visual network was related to lower thalamic volumes. Beta band functional connectivity correlated positively with disability scores, most prominently in the default mode network, and correlated negatively with cognitive performance in this network. These findings illustrate the relationship between thalamic atrophy, altered functional connectivity and clinical and cognitive dysfunction in MS, which could serve as a bridge to understand how neurodegeneration is associated with altered functional connectivity and subsequently clinical and cognitive decline.

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11 Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

A clustering-based method to detect functional connectivity differences

Recently, resting-state functional magnetic resonance imaging (R-fMRI) has emerged as a powerful tool for investigating functional brain organization changes in a variety of neurological and psychiatric disorders. However, the current techniques may need further development to better define the reference brain networks for quantifying the functional connectivity differences between normal and diseased subject groups. In this study, we introduced a new clustering-based method that can clearly define the reference clusters. By employing group difference information to guide the clustering, the voxels within the reference clusters will have homogeneous functional connectivity changes above predefined levels. This method identified functional clusters that were significantly different between the amnestic mild cognitively impaired (aMCI) and age-matched cognitively normal (CN) subjects. The results indicated that the distribution of the clusters and their functionally disconnected regions resembled the altered memory network regions previously identified in task fMRI studies. In conclusion, the new clustering method provides an advanced approach for studying functional brain organization changes associated with brain diseases.