TheB-matrix must be rotated when correcting for subject motion in DTI data

Fronto-thalamic structural and effective connectivity and delusions in schizophrenia: a combined DTI/DCM study

BACKGROUND

Schizophrenia (SZ) is a complex disorder characterized by a range of behavioral and cognitive symptoms as well as structural and functional alterations in multiple cortical and subcortical structures. SZ is associated with reduced functional network connectivity involving core regions such as the anterior cingulate cortex (ACC) and the thalamus. However, little is known whether effective coupling, the directed influence of one structure over the other, is altered during rest in the ACC-thalamus network.

METHODS

We collected resting-state fMRI and diffusion-weighted MRI data from 18 patients and 20 healthy controls. We analyzed fronto-thalamic effective connectivity using dynamic causal modeling for cross-spectral densities in a network consisting of the ACC and the left and right medio-dorsal thalamic regions. We studied structural connectivity using fractional anisotropy (FA).

RESULTS

We found decreased coupling strength from the right thalamus to the ACC and from the right thalamus to the left thalamus, as well as increased inhibitory intrinsic connectivity in the right thalamus in patients relative to controls. ACC-to-left thalamus coupling strength correlated with the Positive and Negative Syndrome Scale (PANSS) total positive syndrome score and with delusion score. Whole-brain structural analysis revealed several tracts with reduced FA in patients, with a maximum decrease in white matter tracts containing fronto-thalamic and cingulo-thalamic fibers.

CONCLUSIONS

We found altered effective and structural connectivity within the ACC-thalamus network in SZ. Our results indicate that ACC-thalamus network activity at rest is characterized by reduced thalamus-to-ACC coupling. We suggest that positive symptoms may arise as a consequence of compensatory measures to imbalanced fronto-thalamic coupling.

Altered microstructural properties of superficial white matter in patients with Parkinson's disease

Parkinson's disease (PD), a chronic neurodegenerative disease, is characterized by sensorimotor and cognitive deficits. Previous diffusion tensor imaging (DTI) studies found abnormal DTI metrics in white matter bundles, such as the corpus callosum, cingulate, and frontal-parietal bundles, in PD patients. These studies mainly focused on alterations in microstructural features of long-range bundles within the deep white matter (DWM) that connects pairs of distant cortical regions. However, less is known about the DTI metrics of the superficial white matter (SWM) that connects local cortical regions in PD patients. To determine whether the DTI metrics of the SWM were different between the PD patients and the healthy controls, we recruited DTI data from 34 PD patients and 29 gender- and age-matched healthy controls. Using a probabilistic tractographic approach, we first defined a population-based SWM mask across all the subjects. Using a tract-based spatial statistical (TBSS) analytic approach, we then identified the SWM bundles showing abnormal DTI metrics in the PD patients. We found that the PD patients showed significantly lower DTI metrics in the SWM bundles connecting the sensorimotor cortex, cingulate cortex, posterior parietal cortex (PPC), and parieto-occipital cortex than the healthy controls. We also found that the clinical measures in the PD patients was significantly negatively correlated with the fractional anisotropy in the SWM (FA_SWM) that connects core regions in the default mode network (DMN). The FA_SWM in the bundles that connected the PPC was significantly positively correlated with cognitive performance in the PD patients. Our findings suggest that SWM may serve as the brain structural basis underlying the sensorimotor deficits and cognitive degeneration in PD patients.

Reduced Microstructural Lateralization in Males with Chronic Schizophrenia: A Diffusional Kurtosis Imaging Study

Decreased brain lateralization is considered a trait marker of schizophrenia. Whereas reductions in both functional and macrostructural gray matter laterality in schizophrenia are well established, the investigation of gray matter microstructural lateralization has so far been limited to a small number of ex vivo studies, which limits the understanding of neurobiological substrates involved and development of adequate treatments. The aim of the current study was to assess in vivo gray matter microstructure lateralization patterns in schizophrenia by employing the diffusion kurtosis imaging (DKI)-derived mean kurtosis (MK) metric. MK was calculated for 18 right-handed males with chronic schizophrenia and 19 age-matched healthy control participants in 46 bilateral gray matter regions of interest (ROI). Microstructural laterality indexes (μLIs) were calculated for each subject and ROI, and group comparisons were conducted across regions. The relationship between μLI values and performance on the Wisconsin Card Sorting Test (WCST) was also evaluated. We found that compared with healthy controls, males with chronic schizophrenia had significantly decreased μLI across cortical and subcortical gray matter regions, which was correlated with poorer performance on the WCST. Our results suggest the ability of DKI-derived MK to capture gray matter microstructural lateralization pathology in vivo.

Impaired cerebro-cerebellar white matter connectivity and its associations with cognitive function in patients with schizophrenia

Schizophrenia is a complex brain disorder of unknown etiology. Based on the notion of “cognitive dysmetria,” we aimed to investigate aberrations in structural white matter (WM) connectivity that links the cerebellum to cognitive dysfunction in patients with schizophrenia. A total of 112 participants (65 patients with schizophrenia and 47 healthy controls [HCs]) were enrolled and underwent diffusion tensor imaging. Between-group voxel-wise comparisons of cerebellar WM regions (superior/middle [MCP]/inferior cerebellar peduncle and pontine crossing fibers) were performed using Tract-Based Spatial Statistics. Cognitive function was assessed using the Trail Making Test Part A/B (TMT-A/B), Wisconsin Card Sorting Test (WCST), and Rey-Kim Memory Test in 46 participants with schizophrenia. WM connectivity, measured as fractional anisotropy (FA), was significantly lower in the MCP in participants with schizophrenia than in HCs. The mean FAs extracted from the significant MCP cluster were inversely correlated with poorer cognitive performance, particularly longer time to complete the TMB-B (r = 0.559, p < 0.001) and more total errors in the WCST (r = 0.442, p = 0.003). Our findings suggest that aberrant cerebro-cerebellar communication due to disrupted WM connectivity may contribute to cognitive impairments, a core characteristic of schizophrenia. Our results may expand our understanding of the neurobiology of schizophrenia based on the cerebro-cerebellar interconnectivity of the brain.

When functional blurring becomes deleterious: Reduced system segregation is associated with less white matter integrity and cognitive decline in aging

Healthy aging is accompanied by progressive decline in cognitive performance and concomitant changes in brain structure and functional architecture. Age-accompanied alterations in brain function have been characterized on a network level as weaker functional connections within brain networks along with stronger interactions between networks. This phenomenon has been described as age-related differences in functional network segregation. It has been suggested that functional networks related to associative processes are particularly sensitive to age-related deterioration in segregation, possibly related to cognitive decline in aging. However, there have been only a few longitudinal studies with inconclusive results. Here, we used a large longitudinal sample of 284 participants between 25 to 80 years of age at baseline, with cognitive and neuroimaging data collected at up to three time points over a 10-year period. We investigated age-related changes in functional segregation among two large-scale systems comprising associative and sensorimotor-related resting-state networks. We found that functional segregation of associative systems declines in aging with exacerbated deterioration from the late fifties. Changes in associative segregation were positively associated with changes in global cognitive ability, suggesting that decreased segregation has negative consequences for domain-general cognitive functions. Age-related changes in system segregation were partly accounted for by changes in white matter integrity, but white matter integrity only weakly influenced the association between segregation and cognition. Together, these novel findings suggest a cascade where reduced white-matter integrity leads to less distinctive functional systems which in turn contributes to cognitive decline in aging.

The longitudinal relationship between BOLD signal variability changes and white matter maturation during early childhood

Intra-individual transient temporal fluctuations in brain signal, as measured by fMRI blood oxygenation level dependent (BOLD) variability, is increasingly considered an important signal rather than measurement noise. Evidence from computational and cognitive neuroscience suggests that signal variability is a good proxy-measure of brain functional integrity and information processing capacity. Here, we sought to explore across-participant and longitudinal relationships between BOLD variability, age, and white matter structure in early childhood. We measured standard deviation of BOLD signal, total white matter volume, global fractional anisotropy (FA) and mean diffusivity (MD) during passive movie viewing in a sample of healthy children (aged 2-8 years; N = 83). We investigated how age and white matter development related to changes in BOLD variability both across- and within-participants. Our across-participant analyses using behavioural partial least squares (bPLS) revealed that the influence of age and white matter maturation on BOLD variability was highly interrelated. BOLD variability increased in widespread frontal, temporal and parietal regions, and decreased in the hippocampus and parahippocampal gyrus with age and white matter development. Our longitudinal analyses using linear mixed effects modelling revealed significant associations between BOLD variability, age and white matter microstructure. Analyses using artificial neural networks demonstrated that BOLD variability and white matter micro and macro-structure at earlier ages were strong predictors of BOLD variability at later ages. By characterizing the across-participant and longitudinal features of the association between BOLD variability and white matter micro- and macrostructure in early childhood, our results provide a novel perspective to understand structure-function relationships in the developing brain.

Structural impairments in hippocampal and occipitotemporal networks specifically contribute to decline in place and face category processing but not to other visual object categories in healthy aging

BACKGROUND

Functional neuroimaging studies have identified a set of nodes in the occipital-temporal cortex that preferentially respond to faces in comparison with other visual objects. By contrast, the processing of places seems to rely on parahippocampal cortex and structures heavily implicated in memory (e.g., the hippocampus). It has been suggested that human aging leads to decreased neural specialization of core face and place processing areas and impairments in face and place perception.

METHODS

Using mediation analysis, we tested the potential contribution of micro- and macrostructure within the hippocampal and occipitotemporal systems to age-associated effects in face and place category processing (as measured by 1-back working memory tasks) in 55 healthy adults (age range 23-79 years). To test for specific contributions of the studied structures to face/place processing, we also studied a distinct tract (i.e., the anterior thalamic radiation [ATR]) and cognitive performance for other visual object categories (objects, bodies, and verbal material). Constrained spherical deconvolution-based tractography was used to reconstruct the fornix, the inferior longitudinal fasciculus (ILF), and the ATR. Hippocampal volumetric measures were segmented from FSL-FIRST toolbox.

RESULTS

It was found that age associates with (a) decreases in fractional anisotropy (FA) in the fornix, in right ILF (but not left ILF), and in the ATR (b) reduced volume in the right and left hippocampus and (c) decline in visual object category processing. Importantly, mediation analysis showed that micro- and macrostructural impairments in the fornix and right hippocampus, respectively, associated with age-dependent decline in place processing. Alternatively, microstructural impairments in right hemispheric ILF associated with age-dependent decline in face processing. There were no other mediator effects of micro- and macrostructural variables on age-cognition relationships.

CONCLUSION

Together, the findings support specific contributions of the fornix and right hippocampus in visuospatial scene processing and of the long-range right hemispheric occipitotemporal network in face category processing.

Effects of seizure burden on structural global brain networks in patients with unilateral hippocampal sclerosis

BACKGROUND AND PURPOSE

Temporal lobe epilepsy secondary to hippocampal sclerosis is related to epileptogenic networks rather than a focal epileptogenic source. Graph-theoretical gray and white matter networks may help to identify alterations within these epileptogenic networks.

METHODS

Twenty-seven patients with hippocampal sclerosis and 14 controls underwent magnetic resonance imaging, including 3D-T1, fluid-attenuated inversion recovery, and diffusion tensor imaging. Subject-specific structural gray and white matter network properties (normalized path length, clustering, and small-worldness) were reconstructed. Group differences and differences between those with higher and lower seizure burden (<4 vs. ≥4 average monthly seizures in the last year) in network parameters were evaluated. Additionally, correlations between network properties and disease-related variables were calculated.

RESULTS

All patients with hippocampal sclerosis as one group did not have altered gray or white matter network properties (all p > .05). Patients with lower seizure burden had significantly lower gray matter small-worldness and normalized clustering compared to controls and those with higher seizure burden (all p < .04). A higher number of monthly seizures was significantly associated with increased gray and white matter small-worldness, indicating a more rigid network.

CONCLUSION

Overall, there were no differences in network properties in this group of patients with hippocampal sclerosis. However, patients with lower seizure burden had significantly lower gray matter network indices, indicating a more random organization. The correlation between higher monthly seizures and a more rigid network is driven by those with higher seizure burden, who presented a more rigid network compared to those with a lower seizure burden.

The effect of vascular health factors on white matter microstructure mediates age-related differences in executive function performance

Even within healthy aging, vascular risk factors can detrimentally influence cognition, with executive functions (EF) particularly vulnerable. Fronto-parietal white matter (WM) connectivity in part, supports EF and may be particularly sensitive to vascular risk. Here, we utilized structural equation modeling in 184 healthy adults (aged 20-94 years of age) to test the hypotheses that: 1) fronto-parietal WM microstructure mediates age effects on EF; 2) higher blood pressure (BP) and white matter hyperintensity (WMH) burden influences this association. All participants underwent comprehensive cognitive and neuropsychological testing including tests of processing speed, executive function (with a focus on tasks that require switching and inhibition) and completed an MRI scanning session that included FLAIR imaging for semi-automated quantification of white matter hyperintensity burden and diffusion-weighted imaging for tractography. Structural equation models were specified with age (as a continuous variable) and blood pressure predicting within-tract WMH burden and fractional anisotropy predicting executive function and processing speed. Results indicated that fronto-parietal white matter of the genu of the corpus collosum, superior longitudinal fasciculus, and the inferior frontal occipital fasciculus (but not cortico-spinal tract) mediated the association between age and EF. Additionally, increased systolic blood pressure and white matter hyperintensity burden within these white matter tracts contribute to worsening white matter health and are important factors underlying age-brain-behavior associations. These findings suggest that aging brings about increases in both BP and WMH burden, which may be involved in the degradation of white matter connectivity and in turn, negatively impact executive functions as we age.

Combined Support Vector Machine Classifier and Brain Structural Network Features for the Individual Classification of Amnestic Mild Cognitive Impairment and Subjective Cognitive Decline Patients

OBJECTIVE

Individuals with subjective cognitive decline (SCD) or amnestic mild cognitive impairment (aMCI) represent important targets for the early detection and intervention of Alzheimer's disease (AD). In this study, we employed a multi-kernel support vector machine (SVM) to examine whether white matter (WM) structural networks can be used for screening SCD and aMCI.

METHODS

A total of 138 right-handed participants [51 normal controls (NC), 36 SCD, 51 aMCI] underwent MRI brain scans. For each participant, three types of WM networks with different edge weights were constructed with diffusion MRI data: fiber number-weighted networks, mean fractional anisotropy-weighted networks, and mean diffusivity (MD)-weighted networks. By employing a multiple-kernel SVM, we seek to integrate information from three weighted networks to improve classification performance. The accuracy of classification between each pair of groups was evaluated via leave-one-out cross-validation.

RESULTS

For the discrimination between SCD and NC, an area under the curve (AUC) value of 0.89 was obtained, with an accuracy of 83.9%. Further analysis revealed that the methods using three types of WM networks outperformed other methods using single WM network. Moreover, we found that most of discriminative features were from MD-weighted networks, which distributed among frontal lobes. Similar classification performance was also reported in the differentiation between subjects with aMCI and NCs (accuracy = 83.3%). Between SCD and aMCI, an AUC value of 0.72 was obtained, with an accuracy of 72.4%, sensitivity of 74.5% and specificity of 69.4%. The highest accuracy was achieved with features only selected from MD-weighted networks.

CONCLUSION

White matter structural network features help machine learning algorithms accurately identify individuals with SCD and aMCI from NCs. Our findings have significant implications for the development of potential brain imaging markers for the early detection of AD.

Inflammatory reaction in the retina after focal non-convulsive status epilepticus in mice investigated with high resolution magnetic resonance and diffusion tensor imaging

Pathophysiological consequences of focal non-convulsive status epilepticus (fNCSE) have been difficult to demonstrate in humans. In rats fNCSE pathology has been identified in the eyes. Here we evaluated the use of high-resolution 7 T structural T₁-weighted magnetic resonance imaging (MRI) and 9.4 T diffusion tensor imaging (DTI) for detecting hippocampal fNCSE-induced retinal pathology ex vivo in mice. Seven weeks post-fNCSE, increased number of Iba1⁺ microglia were evident in the retina ipsilateral to the hemisphere with fNCSE, and morphologically more activated microglia were found in both ipsi- and contralateral retina compared to non-stimulated control mice. T₁-weighted intensity measurements of the contralateral retina showed a minor increase within the outer nuclear and plexiform layers of the lateral retina. T₁-weighted measurements were not performed in the ipsilateral retina due to technical difficulties. DTI fractional anisotropy(FA) values were discretely altered in the lateral part of the ipsilateral retina and unaltered in the contralateral retina. No changes were observed in the distal part of the optic nerve. The sensitivity of both imaging techniques for identifying larger retinal alteration was confirmed ex vivo in retinitis pigmentosa mice where a substantial neurodegeneration of the outer retinal layers is evident. With MR imaging a 50 % decrease in DTI FA values and significantly thinner retina in T₁-weighted images were detected. We conclude that retinal pathology after fNCSE in mice is subtle and present bilaterally. High-resolution T₁-weighted MRI and DTI independently did not detect the entire pathological retinal changes after fNCSE, but the combination of the two techniques indicated minor patchy structural changes.

12-year prediction of mild cognitive impairment aided by Alzheimer's brain signatures at mean age 56

Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer's disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Towards that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: (i) a validated MRI-derived Alzheimer's disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and (ii) a novel grey matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246-367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51-60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer's disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61-71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply ageing-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P = 0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step towards improving very early identification of Alzheimer's disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.

DTI-derived parameters differ between moderate and severe traumatic brain injury and its association with psychiatric scores

BACKGROUND AND AIM

Diffusion tensor imaging (DTI) parameters in the corpus callosum have been suggested to be a biomarker for prognostic outcomes in individuals with diffuse axonal injury (DAI). However, differences between the DTI parameters on moderate and severe trauma in DAI over time are still unclear. A secondary goal was to study the association between the changes in the DTI parameters, anxiety, and depressive scores in DAI over time.

METHODS

Twenty subjects were recruited from a neurological outpatient clinic and evaluated at 2, 6, and 12 months after the brain injury and compared to matched age and sex healthy controls regarding the DTI parameters in the corpus callosum. State-Trace Anxiety Inventory and Beck Depression Inventory were used to assess psychiatric outcomes in the TBI group over time.

RESULTS

Differences were observed in the fractional anisotropy and mean diffusivity of the genu, body, and splenium of the corpus callosum between DAI and controls (p < 0.02). Differences in both parameters in the genu of the corpus callosum were also detected between patients with moderate and severe DAI (p < 0.05). There was an increase in the mean diffusivity values and the fractional anisotropy decrease in the DAI group over time (p < 0.02). There was no significant correlation between changes in the fractional anisotropy and mean diffusivity across the study and psychiatric outcomes in DAI.

CONCLUSION

DTI parameters, specifically the mean diffusivity in the corpus callosum, may provide reliable characterization and quantification of differences determined by the brain injury severity. No correlation was observed with DAI parameters and the psychiatric outcome scores.

Rates of cognitive impairment in a South African cohort of people with HIV: variation by definitional criteria and lack of association with neuroimaging biomarkers

There is wide variation in the reported prevalence of cognitive impairment in people with HIV (PWH). Part of this variation may be attributable to different studies using different methods of combining neuropsychological test scores to classify participants as either cognitively impaired or unimpaired. Our aim was to determine, in a South African cohort of PWH (N = 148), (a) how much variation in reported rates was due to method used to define cognitive impairment and (b) which method correlated best with MRI biomarkers of HIV-related brain pathology. Participants completed detailed neuropsychological assessment and underwent 3 T structural MRI and diffusion tensor imaging (DTI). We used the neuropsychological data to investigate 20 different methods of determining HIV-associated cognitive impairment. We used the neuroimaging data to obtain volumes for cortical and subcortical grey matter and total white matter and DTI metrics for several white matter tracts. Applying each of the 20 methods to the cognitive dataset resulted in a wide variation (20-97%) in estimated rates of impairment. Logistic regression models showed no method was associated with HIV-related neuroimaging abnormalities as measured by structural volumes or DTI metrics. We conclude that for the population from which this sample was drawn, much of the variation in reported rates of cognitive impairment in PWH is due to the method of classification used, and that none of these methods accurately reflects biological effects of HIV in the brain. We suggest that defining HIV-associated cognitive impairment using neuropsychological test performance only is insufficient; pre-morbid functioning, co-morbidities, cognitive symptoms, and functional impairment should always be considered.

Multimodal Elements of Suicidality Reduction After Transcranial Magnetic Stimulation

OBJECTIVES

Repetitive transcranial magnetic stimulation (TMS) is a promising treatment for suicidality, but it is underlying neural mechanisms remain poorly understood. Our prior findings indicated that frontostriatal functional connectivity correlates with the severity of suicidal thoughts and behaviors. In this secondary analysis of data from an open label trial, we evaluated whether changes in frontostriatal functional connectivity would accompany suicidality reductions following TMS. We also explored the relationship between frontostriatal connectivity change and underlying white matter (WM) organization.

MATERIALS AND METHODS

We conducted seed-based functional connectivity analysis on participants (N = 25) with comorbid post-traumatic stress disorder and depression who received eight weeks of 5 Hz TMS to left dorsolateral prefrontal cortex. We measured clinical symptoms with the Inventory of Depressive Symptomatology-Self Report (IDS-SR) and the PTSD Checklist for DSM-5 (PCL-5). We derived suicidality from IDS-SR item 18. Magnetic resonance imaging data were collected before TMS, and at treatment end point. These data were entered into analyses of covariance, evaluating the effect of suicidality change across treatment on striatal and thalamic functional connectivity. Changes in other PTSD and depression symptoms were included as covariates and results were corrected for multiple comparisons. Diffusion connectometry in a participant subsample (N = 17) explored the relationship between frontal WM integrity at treatment baseline and subsequent functional connectivity changes correlated with differences in suicidality.

RESULTS

Suicidal ideation decreased in 65% of participants. Reductions in suicidality and functional connectivity between the dorsal striatum and frontopolar cortex were correlated (p-False Discover Rate-corrected < 0.001), after covariance for clinical symptom change. All other results were nonsignificant. Our connectometry results indicated that the integrity of frontostriatal WM may circumscribe functional connectivity response to TMS for suicide.

CONCLUSIONS

Targeted reduction of fronto-striatal connectivity with TMS may be a promising treatment for suicidality. Future research can build on this multimodal approach to advance individualized stimulation approaches in high-risk patients.

White matter microstructure in youth at risk for serious mental illness: A comparative analysis

Identifying biomarkers of serious mental illness, such as altered white matter microstructure, can aid in early diagnosis and treatment. White matter microstructure was assessed using constrained spherical deconvolution of diffusion imaging data in a sample of 219 youth (age 12-25 years, 64.84% female) across 8 sites. Participants were classified as healthy controls (HC; n = 47), familial risk for serious mental illness (n = 31), mild-symptoms (n = 37), attenuated syndromes (n = 66), or discrete disorder (n = 38) based on clinical assessments. Fractional anisotropy (FA) and mean diffusivity (MD) values were derived for the whole brain white matter, forceps minor, anterior cingulate, anterior thalamic radiations (ATR), inferior fronto-occipital fasciculus, superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF). Linear mixed effects models showed a significant effect of age on MD of the left ATR, left SLF, and left UF, and a significant effect of group on FA for all tracts examined. For most tracts, the discrete disorder group had significantly lower FA than other groups, and the attenuated syndromes group had higher FA compared to HC, with few differences between the remaining groups. White matter differences in MDD are most evident in individuals following illness onset, as few significant differences were observed in the risk phase.

Vocal music listening enhances post-stroke language network reorganization

Listening to vocal music has been recently shown to improve language recovery in stroke survivors. The neuroplasticity mechanisms supporting this effect are, however, still unknown. Using data from a three-arm single-blind randomized controlled trial including acute stroke patients (N=38) and a 3-month follow-up, we set out to compare the neuroplasticity effects of daily listening to self-selected vocal music, instrumental music, and audiobooks on both brain activity and structural connectivity of the language network. Using deterministic tractography we show that the 3-month intervention induced an enhancement of the microstructural properties of the left frontal aslant tract (FAT) for the vocal music group as compared to the audiobook group. Importantly, this increase in the strength of the structural connectivity of the left FAT correlated with improved language skills. Analyses of stimulus-specific activation changes showed that the vocal music group exhibited increased activations in the frontal termination points of the left FAT during vocal music listening as compared to the audiobook group from acute to 3-month post-stroke stage. The increased activity correlated with the structural neuroplasticity changes in the left FAT. These results suggest that the beneficial effects of vocal music listening on post-stroke language recovery are underpinned by structural neuroplasticity changes within the language network and extend our understanding of music-based interventions in stroke rehabilitation.Significance statementPost-stroke language deficits have a devastating effect on patients and their families. Current treatments yield highly variable outcomes and the evidence for their long-term effects is limited. Patients often receive insufficient treatment that are predominantly given outside the optimal time window for brain plasticity. Post-stroke vocal music listening improves language outcome which is underpinned by neuroplasticity changes within the language network. Vocal music listening provides a complementary rehabilitation strategy which could be safely implemented in the early stages of stroke rehabilitation and seems to specifically target language symptoms and recovering language network.

Evaluation of a connectivity-based imaging metric that reflects functional decline in Multiple Sclerosis

Cognitive impairment is a common symptom in individuals with Multiple Sclerosis (MS), but meaningful, reliable biomarkers relating to cognitive decline have been elusive, making evaluation of the impact of therapeutics on cognitive function difficult. Here, we combine pathway-based MRI measures of structural and functional connectivity to construct a metric of functional decline in MS. The Structural and Functional Connectivity Index (SFCI) is proposed as a simple, z-scored metric of structural and functional connectivity, where changes in the metric have a simple statistical interpretation and may be suitable for use in clinical trials. Using data collected at six time points from a 2-year longitudinal study of 20 participants with MS and 9 age- and sex-matched healthy controls, we probe two common symptomatic domains, motor and cognitive function, by measuring structural and functional connectivity in the transcallosal motor pathway and posterior cingulum bundle. The SFCI is significantly lower in participants with MS compared to controls (p = 0.009) and shows a significant decrease over time in MS (p = 0.012). The change in SFCI over two years performed favorably compared to measures of brain parenchymal fraction and lesion volume, relating to follow-up measures of processing speed (r = 0.60, p = 0.005), verbal fluency (r = 0.57, p = 0.009), and score on the Multiple Sclerosis Functional Composite (r = 0.67, p = 0.003). These initial results show that the SFCI is a suitable metric for longitudinal evaluation of functional decline in MS.

Diffusion tensor imaging of the anterior cruciate ligament following primary repair with internal bracing: A longitudinal study

Diffusion tensor imaging (DTI) provides information about tissue microstructure and its degree of organization by quantifying water diffusion. We aimed to monitor longitudinal changes in DTI parameters (fractional isotropy, FA; mean diffusivity, MD; axial diffusivity, AD; radial diffusivity, RD) of the anterior cruciate ligament (ACL) following primary repair with internal bracing (IBLA). Fourteen patients undergoing IBLA were enrolled prospectively and scheduled for clinical follow-up, including instrumented laxity testing, and DTI at 3, 6, 12, and 24 months postoperatively. DTI was also performed in seven healthy subjects. Fiber tractography was used for 3D segmentation of the whole ACL volume, from which median DTI parameters were calculated. The posterior cruciate ligament (PCL) served as a control. Longitudinal DTI changes were assessed using a linear mixed model, and repeated measures correlations were calculated between DTI parameters and clinical laxity tests. At follow-up, thirteen patients had a stable knee and one patient sustained an ACL rerupture after 12 months postoperatively. The ACL repair showed a significant decrease of FA within the first 12 months after surgery, followed by stable FA values thereafter, while ACL diffusivities decreased over time returning towards normal values at 24 months postoperatively. For PCL there were no significant DTI changes over time. There was a significant correlation between ACL FA and laxity tests (r = -0.42, P = .017). This study has shown the potential of DTI to longitudinally monitor diffusion changes in the ACL following IBLA. The DTI findings suggest that healing of the ACL repair is incomplete at 24 months postoperatively.

Trigeminal neuralgia diffusivities using Gaussian process classification and merged group tractography

ABSTRACT

Imaging of trigeminal neuralgia (TN) has demonstrated key diffusion tensor imaging-based diffusivity alterations in the trigeminal nerve; however, imaging has primarily focused on the peripheral nerve segment because of previous limitations in reliably segmenting small fiber bundles across multiple subjects. We used Selective Automated Group Integrated Tractography to study 36 subjects with TN (right-sided pain) and 36 sex-matched controls to examine the trigeminal nerve (fifth cranial nerve [CN V]), pontine decussation (TPT), and thalamocortical fibers (S1). Gaussian process classifiers were trained by scrolling a moving window over CN V, TPT, and S1 tractography centroids. Fractional anisotropy (FA), generalized FA, radial diffusivity, axial diffusivity, and mean diffusivity metrics were evaluated for both groups, analyzing TN vs control groups and affected vs unaffected sides. Classifiers that performed at greater-than-or-equal-to 70% accuracy were included. Gaussian process classifier consistently demonstrated bilateral trigeminal changes, differentiating them from controls with an accuracy of 80%. Affected and unaffected sides could be differentiated from each other with 75% accuracy. Bilateral TPT could be distinguished from controls with at least 85% accuracy. TPT left-right classification achieved 98% accuracy. Bilateral S1 could be differentiated from controls, where the affected S1 radial diffusivity classifier achieved 87% accuracy. This is the first TN study that combines group-wise merged tractography, machine learning classification, and analysis of the complete trigeminal pathways from the peripheral fibers to S1 cortex. This analysis demonstrates that TN is characterized by bilateral abnormalities throughout the trigeminal pathway compared with controls and abnormalities between affected and unaffected sides. This full pathway tractography study of TN demonstrates bilateral changes throughout the trigeminal pathway and changes between affected and unaffected sides.

Decreased Microstructural Integrity of the Central Somatosensory Tracts in Diabetic Peripheral Neuropathy

CONTEXT

Although diabetic peripheral neuropathy (DPN) is predominantly considered a disorder of the peripheral nerves, some evidence for central nervous system involvement has recently emerged. However, whether or to what extent the microstructure of central somatosensory tracts may be injured remains unknown.

OBJECTIVE

This work aimed to detect the microstructure of central somatosensory tracts in type 2 diabetic patients and to correlate it with the severity of DPN.

METHODS

A case-control study at a tertiary referral hospital took place with 57 individuals with type 2 diabetes (25 with DPN, 32 without DPN) and 33 nondiabetic controls. The fractional anisotropy (FA) values of 2 major somatosensory tracts (the spinothalamic tract and its thalamocortical [spino-thalamo-cortical, STC] pathway, the medial lemniscus and its thalamocortical [medial lemnisco-thalamo-cortical, MLTC] pathway) were assessed based on diffusion tensor tractography. Regression models were further applied to detect the association of FA values with the severity of DPN in diabetic patients.

RESULTS

The mean FA values of left STC and left MLTC pathways were significantly lower in patients with DPN than those without DPN and controls. Moreover, FA values of left STC and left MLTC pathways were significantly associated with the severity of DPN (expressed as Toronto Clinical Scoring System values) in patients after adjusting for multiple confounders.

CONCLUSION

Our findings demonstrated the axonal degeneration of central somatosensory tracts in type 2 diabetic patients with DPN. The parallel disease progression of the intracranial and extracranial somatosensory system merits further attention to the central nerves in diabetic patients with DPN.

White matter connectivity for early prediction of Alzheimer's disease

BACKGROUND

Early diagnosis of Alzheimer's disease (AD) remains challenging. It is speculated that structural atrophy in white matter tracts commences prior to the onset of AD symptoms.

OBJECTIVE

We hypothesize that disruptions in white matter tract connectivity precedes the onset of AD symptoms and these disruptions could be leveraged for early prediction of AD.

METHODS

Diffusion tensor images (DTI) from 52 subjects with mild cognitive impairment (MCI) were selected. Subjects were dichotomized into two age and gender matched groups; the MCI-AD group (22 subjects who progressed to develop AD) and the MCI-control group (who did not develop AD). DTI images were anatomically parcellated into 90 distinct regions ROIs followed by tractography methods to obtain different biophysical networks. Features extracted from these networks were used to train predictive algorithms with the objective of discriminating the MCI-AD and MCI-control groups. Model performance and best features are reported.

RESULTS

Up to 80% prediction accuracy was achieved using a combination of features from the 'right anterior cingulum' and 'right frontal superior medial'. Additionally, local network features were more useful than global in improving the model's performance.

CONCLUSION

Connectivity-based characterization of white matter tracts offers potential for early detection of MCI-AD and in the discovery of novel imaging biomarkers.

The role of the orbitofrontal cortex and the nucleus accumbens for craving in alcohol use disorder

This study aimed to investigate structural and functional alterations of the reward system and the neurobiology of craving in alcohol use disorder (AUD). We hypothesized reduced volume of the nucleus accumbens (NAcc), reduced structural connectivity of the segment of the supero-lateral medial forebrain bundle connecting the orbitofrontal cortex (OFC) with the NAcc (OFC-NAcc), and reduced resting-state OFC-NAcc functional connectivity (FC). Furthermore, we hypothesized that craving is related to an increase of OFC-NAcc FC. Thirty-nine recently abstinent patients with AUD and 18 healthy controls (HC) underwent structural (T1w-MP2RAGE, diffusion-weighted imaging (DWI)) and functional (resting-state fMRI) MRI-scans. Gray matter volume of the NAcc, white matter microstructure (fractional anisotropy (FA)) and macrostructure (tract length) of the OFC-NAcc connection and OFC-NAcc FC were compared between AUD and HC using a mixed model MANCOVA controlling for age and gender. Craving was assessed using the thoughts subscale of the obsessive-compulsive drinking scale (OCDS) scale and was correlated with OFC-NAcc FC. There was a significant main effect of group. Results were driven by a volume reduction of bilateral NAcc, reduced FA in the left hemisphere, and reduced tract length of bilateral OFC-NAcc connections in AUD patients. OFC-NAcc FC did not differ between groups. Craving was associated with increased bilateral OFC-NAcc FC. In conclusion, reduced volume of the NAcc and reduced FA and tract length of the OFC-NAcc network suggest structural alterations of the reward network in AUD. Increased OFC-NAcc FC is associated with craving in AUD, and may contribute to situational alcohol-seeking behavior in AUD.

Structural Gray and White Matter Correlates of Awareness in Alzheimer's Disease

BACKGROUND

Unawareness of disease is a common feature of Alzheimer's disease (AD), but few studies explored its neural correlates. Additionally, neural correlates according to the object of awareness are unexplored.

OBJECTIVE

To investigate structural brain correlates in relation to different objects of awareness.

METHODS

27 people with AD underwent MRI scanning on a 3T Siemens Prisma. T1-MPRAGE was used to investigate cortical thickness and white matter microstructure was defined by DTI as fractional anisotropy, mean, axial, and radial diffusivity. Preprocessing used FreeSurfer6.0, ExploreDTI, and FSL-TBSS. Awareness of disease, cognitive deficits, emotional state, relationships, and functional capacity were assessed with the short version of the Assessment Scale of Psychosocial Impact of the Diagnosis of Dementia. Voxel-wise correlations between brain structure and awareness were determined by FSL-PALM. Analyses were corrected for multiple comparisons using Threshold Free Cluster Enhancement and FWE.

RESULTS

Lower left hemisphere cortical thickness was related to poorer disease awareness uncorrected and corrected for age, sex, and MMSE. In the uncorrected model, mainly right-sided, but also left temporal lower cortical thickness was related to decreased awareness of cognitive deficits. Correcting for age, sex, and MMSE eliminated correlations for the right hemisphere, but extensive correlations in the left hemisphere remained. For white matter integrity, higher right hemisphere MD was related to lower cognitive awareness deficits, and lower FA was related to lower functional capacity awareness.

CONCLUSION

Findings suggest that extensive regions of the brain are linked to self-awareness, with particular frontal and temporal alterations leading to unawareness, in agreement with theoretical models indicating executive and mnemonic forms of anosognosia in AD.

In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution

We present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy participant. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.

Macro- and micro-structural cerebellar and cortical characteristics of cognitive empathy towards fictional characters in healthy individuals

Few investigations have analyzed the neuroanatomical substrate of empathic capacities in healthy subjects, and most of them have neglected the potential involvement of cerebellar structures. The main aim of the present study was to investigate the associations between bilateral cerebellar macro- and micro-structural measures and levels of cognitive and affective trait empathy (measured by Interpersonal Reactivity Index, IRI) in a sample of 70 healthy subjects of both sexes. We also estimated morphometric variations of cerebral Gray Matter structures, to ascertain whether the potential empathy-related peculiarities in cerebellar areas were accompanied by structural differences in other cerebral regions. At macro-structural level, the volumetric differences were analyzed by Voxel-Based Morphometry (VBM)- and Region of Interest (ROI)-based approaches, and at a micro-structural level, we analyzed Diffusion Tensor Imaging (DTI) data, focusing in particular on Mean Diffusivity and Fractional Anisotropy. Fantasy IRI-subscale was found to be positively associated with volumes in right cerebellar Crus 2 and pars triangularis of inferior frontal gyrus. The here described morphological variations of cerebellar Crus 2 and pars triangularis allow to extend the traditional cortico-centric view of cognitive empathy to the cerebellar regions and indicate that in empathizing with fictional characters the cerebellar and frontal areas are co-recruited.

Preserved fractal character of structural brain networks is associated with covert consciousness after severe brain injury

Self-similarity is ubiquitous throughout natural phenomena, including the human brain. Recent evidence indicates that fractal dimension of functional brain networks, a measure of self-similarity, is diminished in patients diagnosed with disorders of consciousness arising from severe brain injury. Here, we set out to investigate whether loss of self-similarity is observed in the structural connectome of patients with disorders of consciousness. Using diffusion MRI tractography from N = 11 patients in a minimally conscious state (MCS), N = 10 patients diagnosed with unresponsive wakefulness syndrome (UWS), and N = 20 healthy controls, we show that fractal dimension of structural brain networks is diminished in DOC patients. Remarkably, we also show that fractal dimension of structural brain networks is preserved in patients who exhibit evidence of covert consciousness by performing mental imagery tasks during functional MRI scanning. These results demonstrate that differences in fractal dimension of structural brain networks are quantitatively associated with chronic loss of consciousness induced by severe brain injury, highlighting the close connection between structural organisation of the human brain and its ability to support cognitive function.

Cost-efficiency trade-offs of the human brain network revealed by a multiobjective evolutionary algorithm

It is widely believed that the formation of brain network architecture is under the pressure of optimal trade-off between reducing wiring cost and promoting communication efficiency. However, the questions of whether this trade-off exists in empirical human brain structural networks and, if so, how it takes effect are still not well understood. Here, we employed a multiobjective evolutionary algorithm to directly and quantitatively explore the cost-efficiency trade-off in human brain structural networks. Using this algorithm, we generated a population of synthetic networks with optimal but diverse cost-efficiency trade-offs. It was found that these synthetic networks could not only reproduce a large portion of connections in the empirical brain structural networks but also embed a resembling small-world organization. Moreover, the synthetic and empirical brain networks were found similar in terms of the spatial arrangement of hub regions and the modular structure, which are two important topological features widely assumed to be outcomes of cost-efficiency trade-offs. The synthetic networks had high robustness against random attacks as the empirical brain networks did. Additionally, we also revealed some differences between the synthetic networks and the empirical brain networks, including lower segregated processing capacity and weaker robustness against targeted attacks in the synthetic networks. These findings provide direct and quantitative evidence that the structure of human brain networks is indeed largely influenced by optimal cost-efficiency trade-offs. We also suggest that some additional factors (e.g., segregated processing capacity) might jointly determine the network organization with cost and efficiency.

Strain Tensor Imaging: Cardiac-induced brain tissue deformation in humans quantified with high-field MRI

The cardiac cycle induces blood volume pulsations in the cerebral microvasculature that cause subtle deformation of the surrounding tissue. These tissue deformations are highly relevant as a potential source of information on the brain's microvasculature as well as of tissue condition. Besides, cyclic brain tissue deformations may be a driving force in clearance of brain waste products. We have developed a high-field magnetic resonance imaging (MRI) technique to capture these tissue deformations with full brain coverage and sufficient signal-to-noise to derive the cardiac-induced strain tensor on a voxel by voxel basis, that could not be assessed non-invasively before. We acquired the strain tensor with 3 mm isotropic resolution in 9 subjects with repeated measurements for 8 subjects. The strain tensor yielded both positive and negative eigenvalues (principle strains), reflecting the Poison effect in tissue. The principle strain associated with expansion followed the known funnel shaped brain motion pattern pointing towards the foramen magnum. Furthermore, we evaluate two scalar quantities from the strain tensor: the volumetric strain and octahedral shear strain. These quantities showed consistent patterns between subjects, and yielded repeatable results: the peak systolic volumetric strain (relative to end-diastolic strain) was 4.19⋅10^-4 ± 0.78⋅10^-4 and 3.98⋅10^-4 ± 0.44⋅10^-4 (mean ± standard deviation for first and second measurement, respectively), and the peak octahedral shear strain was 2.16⋅10^-3 ± 0.31⋅10^-3 and 2.31⋅10^-3 ± 0.38⋅10^-3, for the first and second measurement, respectively. The volumetric strain was typically highest in the cortex and lowest in the periventricular white matter, while anisotropy was highest in the subcortical white matter and basal ganglia. This technique thus reveals new, regional information on the brain's cardiac-induced deformation characteristics, and has the potential to advance our understanding of the role of microvascular pulsations in health and disease.

Multimodal brain imaging connectivity analyses of emotional and motivational deficits in depression among women

BACKGROUND

Major depressive disorder (MDD) is characterized by impaired cortical-subcortical functional connectivity. Apathy adds to functional impairment, but its cerebral basis in MDD remains unknown. Our objective was to describe impairments in functional connectivity during emotional processing in MDD (with varying levels of congruency and attention), and to determine their correlation with apathy.

METHODS

We used the Variable Attention Affective Task during functional MRI, followed by diffusion-weighted MRI, to assess 55 right-handed women (30 with MDD and 25 healthy controls) between September 2012 and February 2015. We estimated functional connectivity using generalized psychophysiologic interaction and anatomic connectivity with tract-based spatial statistics. We measured apathy using the Apathy Evaluation Scale.

RESULTS

We found decreased functional connectivity between the left amygdala and the left anterior cingulate cortex (ACC) during negative stimuli in participants with MDD (t54 = 4.2; p = 0.035, family-wise error [FWE]-corrected). During high-attention stimuli, participants with MDD showed reduced functional connectivity between the right dorsolateral prefrontal cortex (dlPFC) and the right ACC (t54 = 4.06, pFWE = 0.02), but greater functional connectivity between the right dlPFC and the right amygdala (t54 = 3.35, p = 0.048). Apathy was associated with increased functional connectivity between the right dlPFC and the right ACC during high-attention stimuli (t28 = 5.2, p = 0.01) and increased fractional anisotropy in the right posterior cerebellum, the anterior and posterior cingulum and the bilateral internal capsule (all pFWE < 0.05).

LIMITATIONS

Limitations included a moderate sample size, concomitant antidepressant therapy and no directed connectivity.

CONCLUSION

We found that MDD was associated with impairments in cortical-subcortical functional connectivity during negative stimuli that might alter the recruitment of networks engaged in attention. Apathy-related features suggested networks similar to those observed in degenerative disorders, but possible different mechanisms.

Preserved cholinergic forebrain integrity reduces structural connectome vulnerability in mild cognitive impairment

Neurodegeneration leads to redistribution of processing, which is reflected in a reorganisation of the structural connectome. This might affect its vulnerability to structural damage. Cortical acetylcholine allows favourable adaptation to pathology within the memory circuit. However, it remains unclear if it acts on a broader scale, affecting reconfiguration of whole-brain networks. To investigate the role of the cholinergic basal forebrain (CBFB) in strategic lesions, twenty patients with mild cognitive impairment (MCI) and twenty elderly controls underwent magnetic resonance imaging. Whole-brain tractograms were represented as network graphs. Lesions of individual nodes were simulated by removing a node and its connections from the graph. The impact of simulated lesions was quantified as the proportional change in global efficiency. Relationships between subregional CBFB volumes, global efficiency of intact connectomes and impacts of individual simulated lesions of network nodes were assessed. In MCI but not controls, larger CBFB volumes were associated with efficient network topology and reduced impact of hippocampal, thalamic and entorhinal lesions, indicating a protective effect against the global impact of simulated strategic lesions. This suggests that the cholinergic system shapes the configuration of the connectome, thereby reducing the impact of localised damage in MCI.

Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease

Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer's disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature rescaling (FR), feature selection (FS) and cross-validation (CV) procedures. Moreover, these studies are also difficult to reproduce because these different components are not readily available. In a previous work (Samper-González et al. 2018), we propose an open-source framework for the reproducible evaluation of AD classification from T1-weighted (T1w) MRI and PET data. In the present paper, we first extend this framework to diffusion MRI data. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally gives better performance than regional features. Fractional anisotropy (FA) and mean diffusivity (MD) provided comparable results for voxel-wise features. Moreover, we observe that the poor performance obtained in tasks involving MCI were potentially caused by the small data samples, rather than by the data imbalance. Furthermore, no extensive classification difference exists for different degree of smoothing and registration methods. Besides, we demonstrate that using non-nested validation of FS leads to unreliable and over-optimistic results: 5% up to 40% relative increase in BA. Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI. All the code of the framework and the experiments are publicly available: general-purpose tools have been integrated into the Clinica software package ( www.clinica.run ) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML .

Tractography Processing with the Sparse Closest Point Transform

We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative to these landmarks with a closest point transform. We explore its use in three typical tasks: fiber bundle clustering, simplification, and selection across a population. The clustering algorithm groups fibers from single whole-brain datasets using a non-parametric k-means clustering algorithm, with performance compared with three alternative methods and across four datasets. The simplification algorithm removes redundant curves to improve interactive visualization, with performance gauged relative to random subsampling. The selection algorithm extracts bundles across a population using a one-class Gaussian classifier derived from an atlas prototype, with performance gauged by scan-rescan reliability and sensitivity to normal aging, as compared to manual mask-based selection. Our results demonstrate how the SCPT enables the novel application of existing vector-space ML algorithms to create effective and efficient tools for tractography processing. Our experimental data is available online, and our software implementation is available in the Quantitative Imaging Toolkit.

Multivariate white matter alterations are associated with epilepsy duration

Previous studies investigating associations between white matter alterations and duration of temporal lobe epilepsy (TLE) have shown differing results, and were typically limited to univariate analyses of tracts in isolation. In this study, we apply a multivariate measure (the Mahalanobis distance), which captures the distinct ways white matter may differ in individual patients, and relate this to epilepsy duration. Diffusion MRI, from a cohort of 94 subjects (28 healthy controls, 33 left-TLE and 33 right-TLE), was used to assess the association between tract fractional anisotropy (FA) and epilepsy duration. Using ten white matter tracts, we analysed associations using the traditional univariate analysis (z-scores) and a complementary multivariate approach (Mahalanobis distance), incorporating multiple white matter tracts into a single unified analysis. For patients with right-TLE, FA was not significantly associated with epilepsy duration for any tract studied in isolation. For patients with left-TLE, the FA of two limbic tracts (ipsilateral fornix, contralateral cingulum gyrus) were significantly negatively associated with epilepsy duration (Bonferonni corrected p < .05). Using a multivariate approach we found significant ipsilateral positive associations with duration in both left, and right-TLE cohorts (left-TLE: Spearman's ρ = 0.487, right-TLE: Spearman's ρ = 0.422). Extrapolating our multivariate results to duration equals zero (i.e., at onset) we found no significant difference between patients and controls. Associations using the multivariate approach were more robust than univariate methods. The multivariate Mahalanobis distance measure provides non-overlapping and more robust results than traditional univariate analyses. Future studies should consider adopting both frameworks into their analysis in order to ascertain a more complete understanding of epilepsy progression, regardless of laterality.

The dissociative subtype of posttraumatic stress disorder is associated with subcortical white matter network alterations

Posttraumatic stress disorder (PTSD) is characterized by intrusions, avoidance, and hyperarousal while patients of the dissociative subtype (PTSD-D) experience additional dissociative symptoms. A neurobiological model proposes hyper-inhibition of limbic structures mediated by prefrontal cortices to underlie dissociation in PTSD. Here, we tested whether functional alterations in fronto-limbic circuits are underpinned by white matter network abnormalities on a network level. 23 women with PTSD-D and 19 women with classic PTSD participated. We employed deterministic diffusion tractography and graph theoretical analyses. Mean fractional anisotropy (FA) was chosen as a network weight and group differences assessed using network-based statistics. No significant white matter network alterations comprising both frontal and limbic structures in PTSD-D relative to classic PTSD were found. A subsequent whole brain exploratory analysis revealed relative FA alterations in PTSD-D in two subcortical networks, comprising connections between the left amygdala, hippocampus, and thalamus as well as links between the left ventral diencephalon, putamen, and pallidum, respectively. Dissociative symptom severity in the PTSD-D group correlated with FA values within both networks. Our findings suggest fronto-limbic inhibition in PTSD-D may present a dynamic neural process, which is not hard-wired via white matter tracts. Our exploratory results point towards altered fiber tract communication in a limbic-thalamic circuit, which may underlie (a) an initial strong emotional reaction to trauma reminders before conscious regulatory processes are enabled and (b) deficits in early sensory processing. In addition, aberrant structural connectivity in low-level motor regions may present neural correlates for dissociation as a passive threat-response.

Altered properties of brain white matter structural networks in patients with nasopharyngeal carcinoma after radiotherapy

Previous neuroimaging studies revealed radiation-induced brain injury in patients with nasopharyngeal carcinoma (NPC) in the years after radiotherapy (RT). These injuries may be associated with structural and functional alterations. However, differences in the brain structural connectivity of NPC patients at different times after RT, especially in the early-delayed period, remain unclear. We acquired diffusion tensor imaging (DTI) data from three groups of NPC patients, 25 in the pre-RT (before RT) group, 22 in the early-delayed (1-6 months) period (post-RT-ED) group, and 33 in the late-delayed (>6 months) period (post-RT-LD) group. Then, we constructed brain white matter (WM) structural networks and used graph theory to compare their between-group differences. The NPC patients in the post-RT-ED group showed decreased global properties when compared with the pre-RT group. We also detected the nodes with between-group differences in nodal parameters. The nodes that differed between the post-RT-ED and pre-RT groups were mainly located in the default mode (DMN) and central executive networks (CEN); those that differed between the post-RT-LD and pre-RT groups were located in the limbic system; and those that differed between the post-RT-LD and post-RT-ED groups were mainly in the DMN. These findings may indicate that radiation-induced brain injury begins in the early-delayed period and that a reorganization strategy begins in the late-delayed period. Our findings may provide new insight into the pathogenesis of radiation-induced brain injury in normal-appearing brain tissue from the network perspective.

Diffusion Kurtosis Imaging Fiber Tractography of Major White Matter Tracts in Neurosurgery

Diffusion tensor imaging (DTI)-based fiber tractography is routinely used in clinical applications to visualize major white matter tracts, such as the corticospinal tract (CST), optic radiation (OR), and arcuate fascicle (AF). Nevertheless, DTI is limited due to its capability of resolving intra-voxel multi-fiber populations. Sophisticated models often require long acquisition times not applicable in clinical practice. Diffusion kurtosis imaging (DKI), as an extension of DTI, combines sophisticated modeling of the diffusion process with short acquisition times but has rarely been investigated in fiber tractography. In this study, DTI- and DKI-based fiber tractography of the CST, OR, and AF was investigated in healthy volunteers and glioma patients. For the CST, significantly larger tract volumes were seen in DKI-based fiber tractography. Similar results were obtained for the OR, except for the right OR in patients. In the case of the AF, results of both models were comparable with DTI-based fiber tractography showing even significantly larger tract volumes in patients. In the case of the CST and OR, DKI-based fiber tractography contributes to advanced visualization under clinical time constraints, whereas for the AF, other models should be considered.

Locomotor Adaptation Is Associated with Microstructural Properties of the Inferior Cerebellar Peduncle

In sensorimotor adaptation paradigms, participants learn to adjust their behavior in response to an external perturbation. Locomotor adaptation and reaching adaptation depend on the cerebellum and are accompanied by changes in functional connectivity in cortico-cerebellar circuits. In order to gain a better understanding of the particular cerebellar projections involved in locomotor adaptation, we assessed the contribution of specific white matter pathways to the magnitude of locomotor adaptation and to long-term motor adaptation effects (recall and relearning). Diffusion magnetic resonance imaging with deterministic tractography was used to delineate the inferior and superior cerebellar peduncles (ICP, SCP) and the corticospinal tract (CST). Correlations were calculated to assess the association between the diffusivity values along the tracts and behavioral measures of locomotor adaptation. The results point to a significant correlation between the magnitude of adaptation and diffusivity values in the left ICP. Specifically, a higher magnitude of adaptation was associated with higher mean diffusivity and with lower anisotropy values in the left ICP, but not in other pathways. Post hoc analysis revealed that the effect stems from radial, not axial, diffusivity. The magnitude of adaptation was further associated with the degree of ICP lateralization, such that greater adaptation magnitude was correlated with increased rightward asymmetry of the ICP. Our findings suggest that the magnitude of locomotor adaptation depends on afferent signals to the cerebellum, transmitted via the ICP, and point to the contribution of error detection to locomotor adaptation rate.

Motion-insensitive diffusion imaging of the brain using optical tracking and dynamic sequence updates

PURPOSE

Diffusion-weighted imaging (DWI) is sensitive to head movements, which may cause signal losses because of motion-induced gradient imbalances. Prospective motion correction using fast optical tracking can attenuate these artifacts. Approaches include quasicontinuous updates of gradients and radiofrequency (RF) pulses or dynamically applying a rebalancing gradient to restore the gradient balance, but these prior methods used bipolar diffusion gradients. The goal of this project was to develop and evaluate a motion-insensitive implementation for the more common monopolar diffusion sequence.

METHODS

A monopolar diffusion sequence was developed with motion updates before each RF pulse and each diffusion-weighting gradient. The sequence was tested in a phantom and human brain at b = 1000 s/mm² and rotational velocities up to 20°/s. Motion sensitivity, signal losses, and in vivo image profiles were compared between scans with and without intrasequence motion updates.

RESULTS

With typical motion parameters, intrasequence motion updates with optimal parameters reduced the motion sensitivity of DWI (motion-induced gradient moment imbalance) sevenfold. Optimal results were achieved by matching the echo time of the pulse sequence to an even multiple of the tracking system frame-to-frame period. Average signal losses and the frequency of signal dropouts in phantom and in vivo measurements were reduced when intrasequence updates were enabled, and quality measures of DTI analyses were improved.

CONCLUSION

A correction scheme for the monopolar DWI sequence can reduce the motion sensitivity of brain DWI up to sevenfold compared with an implementation without intrasequence updates.

Corticospinal Tract and Related Grey Matter Morphometric Shape Analysis in ALS Phenotypes: A Fractal Dimension Study

A pathological hallmark of amyotrophic lateral sclerosis (ALS) is corticospinal tract (CST) degeneration resulting in upper motor neuron (UMN) dysfunction. No quantitative test is available to easily assess UMN pathways. Brain neuroimaging in ALS promises to potentially change this through identifying biomarkers of UMN dysfunction that may accelerate diagnosis and track disease progression. Fractal dimension (FD) has successfully been used to quantify brain grey matter (GM) and white matter (WM) shape complexity in various neurological disorders. Therefore, we investigated CST and whole brain GM and WM morphometric changes using FD analyses in ALS patients with different phenotypes. We hypothesized that FD would detect differences between ALS patients and neurologic controls and even between the ALS subgroups. Neuroimaging was performed in neurologic controls (n = 14), and ALS patients (n = 75). ALS patients were assigned into four groups based on their clinical or radiographic phenotypes. FD values were estimated for brain WM and GM structures. Patients with ALS and frontotemporal dementia (ALS-FTD) showed significantly higher CST FD values and lower primary motor and sensory cortex GM FD values compared to other ALS groups. No other group of ALS patients revealed significant FD value changes when compared to neurologic controls or with other ALS patient groups. These findings support a more severe disease process in ALS-FTD patients compared to other ALS patient groups. FD value measures may be a sensitive index to evaluate GM and WM (including CST) degeneration in ALS patients.

Combined 18F-FET PET and diffusion kurtosis MRI in posttreatment glioblastoma: differentiation of true progression from treatment-related changes

Background

Radiological differentiation of tumor progression (TPR) from treatment-related changes (TRC) in pretreated glioblastoma is crucial. This study aimed to explore the diagnostic value of diffusion kurtosis MRI combined with information derived from O-(2-[¹⁸F]-fluoroethyl)-l-tyrosine (¹⁸F-FET) PET for the differentiation of TPR from TRC in patients with pretreated glioblastoma.

Methods

Thirty-two patients with histomolecularly defined and pretreated glioblastoma suspected of having TPR were included in this retrospective study. Twenty-one patients were included in the TPR group, and 11 patients in the TRC group, as assessed by neuropathology or clinicoradiological follow-up. Three-dimensional (3D) regions of interest were generated based on increased ¹⁸F-FET uptake using a tumor-to-brain ratio of 1.6. Furthermore, diffusion MRI kurtosis maps were obtained from the same regions of interest using co-registered ¹⁸F-FET PET images, and advanced histogram analysis of diffusion kurtosis map parameters was applied to generated 3D regions of interest. Diagnostic accuracy was analyzed by receiver operating characteristic curve analysis and combinations of PET and MRI parameters using multivariate logistic regression.

Results

Parameters derived from diffusion MRI kurtosis maps show high diagnostic accuracy, up to 88%, for differentiating between TPR and TRC. Logistic regression revealed that the highest diagnostic accuracy of 94% (area under the curve, 0.97; sensitivity, 94%; specificity, 91%) was achieved by combining the maximum tumor-to-brain ratio of ¹⁸F-FET uptake and diffusion MRI kurtosis metrics.

Conclusions

The combined use of ¹⁸F-FET PET and MRI diffusion kurtosis maps appears to be a promising approach to improve the differentiation of TPR from TRC in pretreated glioblastoma and warrants further investigation.

Investigating the brain structural connectome following working memory training in children born extremely preterm or extremely low birth weight

Children born extremely preterm (EP, <28 weeks' gestation) or extremely low birth weight (ELBW, <1,000 g) are a vulnerable population at high risk of working memory impairments. We aimed to examine changes in the brain structural connectivity networks thought to underlie working memory performance, after completion of a working memory training program (Cogmed) compared with a placebo program in EP/ELBW children. This was a double-blind, placebo-controlled randomized trial (the Improving Memory in a Preterm Randomised Intervention Trial). Children born EP/ELBW received either the Cogmed or placebo program at 7 years of age (n = 91). A subset of children had magnetic resonance imaging of the brain immediately pre- and 2 weeks post-training (Cogmed n = 28; placebo n = 27). T₁ -weighted and diffusion-weighted images were used to perform graph theoretical analysis of structural connectivity networks. Changes from pre-training to post-training in structural connectivity metrics were generally similar between randomized groups. There was little evidence that changes in structural connectivity metrics were related to changes in working memory performance from pre- to post-training. Overall, our results provide little evidence that the Cogmed working memory training program has training-specific effects on structural connectivity networks in EP/ELBW children.

Fiber ball white matter modeling in focal epilepsy

Multicompartment diffusion magnetic resonance imaging (MRI) approaches are increasingly being applied to estimate intra‐axonal and extra‐axonal diffusion characteristics in the human brain. Fiber ball imaging (FBI) and its extension fiber ball white matter modeling (FBWM) are such recently described multicompartment approaches. However, these particular approaches have yet to be applied in clinical cohorts. The modeling of several diffusion parameters with interpretable biological meaning may offer the development of new, noninvasive biomarkers of pharmacoresistance in epilepsy. In the present study, we used FBI and FBWM to evaluate intra‐axonal and extra‐axonal diffusion properties of white matter tracts in patients with longstanding focal epilepsy. FBI/FBWM diffusion parameters were calculated along the length of 50 white matter tract bundles and statistically compared between patients with refractory epilepsy, nonrefractory epilepsy and controls. We report that patients with chronic epilepsy had a widespread distribution of extra‐axonal diffusivity relative to controls, particularly in circumscribed regions along white matter tracts projecting to cerebral cortex from thalamic, striatal, brainstem, and peduncular regions. Patients with refractory epilepsy had significantly greater markers of extra‐axonal diffusivity compared to those with nonrefractory epilepsy. The extra‐axonal diffusivity alterations in patients with epilepsy observed in the present study could be markers of neuroinflammatory processes or a reflection of reduced axonal density, both of which have been histologically demonstrated in focal epilepsy. FBI is a clinically feasible MRI approach that provides the basis for more interpretive conclusions about the microstructural environment of the brain and may represent a unique biomarker of pharmacoresistance in epilepsy.

Classification using fractional anisotropy predicts conversion in genetic frontotemporal dementia, a proof of concept

Frontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10–20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to predict clinical conversion in mutation carriers is lacking. In this retrospective proof-of-concept case-control study, we investigate whether MRI-based and cognition-based classifiers can predict which mutation carriers from genetic frontotemporal dementia families will develop symptoms (‘convert’) within 4 years. From genetic frontotemporal dementia families, we included 42 presymptomatic frontotemporal dementia mutation carriers. We acquired anatomical, diffusion-weighted imaging, and resting-state functional MRI, as well as neuropsychological data. After 4 years, seven mutation carriers had converted to frontotemporal dementia (‘converters’), while 35 had not (‘non-converters’). We trained regularized logistic regression models on baseline MRI and cognitive data to predict conversion to frontotemporal dementia within 4 years, and quantified prediction performance using area under the receiver operating characteristic curves. The prediction model based on fractional anisotropy, with highest contribution of the forceps minor, predicted conversion to frontotemporal dementia beyond chance level (0.81 area under the curve, family-wise error corrected P = 0.025 versus chance level). Other MRI-based and cognitive features did not outperform chance level. Even in a small sample, fractional anisotropy predicted conversion in presymptomatic frontotemporal dementia mutation carriers beyond chance level. After validation in larger data sets, conversion prediction in genetic frontotemporal dementia may facilitate early recruitment into clinical trials.

Speech rate association with cerebellar white-matter diffusivity in adults with persistent developmental stuttering

Speech rate is a basic characteristic of language production, which affects the speaker's intelligibility and communication efficiency. Various speech disorders, including persistent developmental stuttering, present altered speech rate. Specifically, adults who stutter (AWS) typically exhibit a slower speech rate compared to fluent speakers. Evidence from imaging studies suggests that the cerebellum contributes to the paced production of speech. People who stutter show structural and functional abnormalities in the cerebellum. However, the involvement of the cerebellar pathways in controlling speech rate remains unexplored. Here, we assess the association of the cerebellar peduncles with speech rate in AWS and control speakers. Diffusion MRI and speech-rate data were collected in 42 participants (23 AWS, 19 controls). We used deterministic tractography with Automatic Fiber segmentation and Quantification (AFQ) to identify the superior, middle, and inferior cerebellar peduncles (SCP, MCP, ICP) bilaterally, and quantified fractional anisotropy (FA) and mean diffusivity (MD) along each tract. No significant differences were observed between AWS and controls in the diffusivity values of the cerebellar peduncles. However, AWS demonstrated a significant negative association between speech rate and FA within the left ICP, a major cerebellar pathway that transmits sensory feedback signals from the olivary nucleus into the cerebellum. The involvement of the ICP in controlling speech production in AWS is compatible with the view that stuttering stems from hyperactive speech monitoring, where even minor deviations from the speech plan are considered as errors. In conclusion, our findings suggest a plausible neural mechanism for speech rate reduction observed in AWS.

In vivo coupling of dendritic complexity with presynaptic density in primary tauopathies

Understanding the cellular underpinnings of neurodegeneration remains a challenge; loss of synapses and dendritic arborization are characteristic and can be quantified in vivo, with [¹¹C]UCB-J PET and MRI-based Orientation Dispersion Imaging (ODI), respectively. We aimed to assess how both measures are correlated, in 4R-tauopathies of progressive supranuclear palsy - Richardson's Syndrome (PSP-RS; n = 22) and amyloid-negative (determined by [¹¹C]PiB PET) Corticobasal Syndrome (Cortiobasal degeneration, CBD; n =14), as neurodegenerative disease models, in this proof-of-concept study. Compared to controls (n = 27), PSP-RS and CBD patients had widespread reductions in cortical ODI, and [¹¹C]UCB-J non-displaceable binding potential (BP_ND) in excess of atrophy. In PSP-RS and CBD separately, regional cortical ODI was significantly associated with [¹¹C]UCB-J BP_ND in disease-associated regions (p < 0.05, FDR corrected). Our findings indicate that reductions in synaptic density and dendritic complexity in PSP-RS and CBD are more severe and extensive than atrophy. Furthermore, both measures are tightly coupled in vivo, furthering our understanding of the pathophysiology of neurodegeneration, and applicable to studies of early neurodegeneration with a safe and widely available MRI platform.

Dynamic changes in white matter following traumatic brain injury and how diffuse axonal injury relates to cognitive domain

Objective: The goal is to evaluate longitudinally with diffusion tensor imaging (DTI) the integrity of cerebral white matter in patients with moderate and severe DAI and to correlate the DTI findings with cognitive deficits.Methods: Patients with DAI (n = 20) were scanned at three timepoints (2, 6 and 12 months) after trauma. A healthy control group (n = 20) was evaluated once with the same high-field MRI scanner. The corpus callosum (CC) and the bilateral superior longitudinal fascicles (SLFs) were assessed by deterministic tractography with ExploreDTI. A neuropschychological evaluation was also performed.Results: The CC and both SLFs demonstrated various microstructural abnormalities in between-groups comparisons. All DTI parameters demonstrated changes across time in the body of the CC, while FA (fractional anisotropy) increases were seen on both SLFs. In the splenium of the CC, progressive changes in the mean diffusivity (MD) and axial diffusivity (AD) were also observed. There was an improvement in attention and memory along time. Remarkably, DTI parameters demonstrated several correlations with the cognitive domains.Conclusions: Our findings suggest that microstructural changes in the white matter are dynamic and may be detectable by DTI throughout the first year after trauma. Likewise, patients also demonstrated improvement in some cognitive skills.

Detecting Corticospinal Tract Impairment in Tumor Patients With Fiber Density and Tensor-Based Metrics

Tumors infiltrating the motor system lead to significant disability, often caused by corticospinal tract injury. The delineation of the healthy-pathological white matter (WM) interface area, for which diffusion magnetic resonance imaging (dMRI) has shown promising potential, may improve treatment outcome. However, up to 90% of white matter (WM) voxels include multiple fiber populations, which cannot be correctly described with traditional metrics such as fractional anisotropy (FA) or apparent diffusion coefficient (ADC). Here, we used a novel fixel-based along-tract analysis consisting of constrained spherical deconvolution (CSD)-based probabilistic tractography and fixel-based apparent fiber density (FD), capable of identifying fiber orientation specific microstructural metrics. We addressed this novel methodology's capability to detect corticospinal tract impairment. We measured and compared tractogram-related FD and traditional microstructural metrics bihemispherically in 65 patients with WHO grade III and IV gliomas infiltrating the motor system. The cortical tractogram seeds were based on motor maps derived by transcranial magnetic stimulation. We extracted 100 equally distributed cross-sections along each streamline of corticospinal tract (CST) for along-tract statistical analysis. Cross-sections were then analyzed to detect differences between healthy and pathological hemispheres. All metrics showed significant differences between healthy and pathologic hemispheres over the entire tract and between peritumoral segments. Peritumoral values were lower for FA and FD, but higher for ADC within the entire cohort. FD was more specific to tumor-induced changes in CST than ADC or FA, whereas ADC and FA showed higher sensitivity. The bihemispheric along-tract analysis provides an approach to detect subject-specific structural changes in healthy and pathological WM. In the current clinical dataset, the more complex FD metrics did not outperform FA and ADC in terms of describing corticospinal tract impairment.

Autistic traits and individual brain differences: functional network efficiency reflects attentional and social impairments, structural nodal efficiencies index systemising and theory-of-mind skills

BACKGROUND

Autism is characterised not only by impaired social cognitive 'empathising' but also by superior rule-based 'systemising'. These cognitive domains intertwine within the categorical diagnosis of autism, yet behavioural genetics suggest largely independent heritability, and separable brain mechanisms. We sought to determine whether quantitative behavioural measures of autistic traits are dimensionally associated with structural and functional brain network integrity, and whether brain bases of autistic traits vary independently across individuals.

METHODS

Thirty right-handed neurotypical adults (12 females) were administered psychometric (Social Responsiveness Scale, Autism Spectrum Quotient and Systemising Quotient) and behavioural (Attention Network Test and theory-of-mind reaction time) measures of autistic traits, and structurally (diffusion tensor imaging) and functionally (500 s of 2 Hz eyes-closed resting fMRI) derived graph-theoretic measures of efficiency of information integration were computed throughout the brain and within subregions.

RESULTS

Social impairment was positively associated with functional efficiency (r = .47, p = .006), globally and within temporo-parietal and prefrontal cortices. Delayed orienting of attention likewise was associated with greater functional efficiency (r = - .46, p = .0133). Systemising was positively associated with global structural efficiency (r = .38, p = 0.018), driven specifically by temporal pole; theory-of-mind reaction time was related to structural efficiency (r = - .40, p = 0.0153) within right supramarginal gyrus.

LIMITATIONS

Interpretation of these relationships is complicated by the many senses of the term 'connectivity', including functional, structural and computational; by the approximation inherent in group functional anatomical parcellations when confronted with individual variation in functional anatomy; and by the validity, sensitivity and specificity of the several survey and experimental behavioural measures applied as correlates of brain structure and function.

CONCLUSIONS

Functional connectivities highlight distributed networks associated with domain-general properties such as attentional orienting and social cognition broadly, associating more impaired behaviour with more efficient brain networks that may reflect heightened feedforward information flow subserving autistic strengths and deficits alike. Structural connectivity results highlight specific anatomical nodes of convergence, reflecting cognitive and neuroanatomical independence of systemising and theory-of-mind. In addition, this work shows that individual differences in theory-of-mind related to brain structure can be measured behaviourally, and offers neuroanatomical evidence to pin down the slippery construct of 'systemising' as the capacity to construct invariant contextual associations.

Altered structural connectome in non-lesional newly diagnosed focal epilepsy: Relation to pharmacoresistance

Despite an expanding literature on brain alterations in patients with longstanding epilepsy, few neuroimaging studies investigate patients with newly diagnosed focal epilepsy (NDfE). Understanding brain network impairments at diagnosis is necessary to elucidate whether or not brain abnormalities are principally due to the chronicity of the disorder and to develop prognostic markers of treatment outcome. Most adults with NDfE do not have MRI-identifiable lesions and the reasons for seizure onset and refractoriness are unknown. We applied structural connectomics to T1-weighted and multi-shell diffusion MRI data with generalized q-sampling image reconstruction using Network Based Statistics (NBS). We scanned 27 patients within an average of 3.7 (SD = 2.9) months of diagnosis and anti-epileptic drug treatment outcomes were collected 24 months after diagnosis. Seven patients were excluded due to lesional NDfE and outcome data was available in 17 patients. Compared to 29 healthy controls, patients with non-lesional NDfE had connectomes with significantly decreased quantitative anisotropy in edges connecting right temporal, frontal and thalamic nodes and increased diffusivity in edges between bilateral temporal, frontal, occipital and parietal nodes. Compared to controls, patients with persistent seizures showed the largest effect size (|d|>=1) for decreased anisotropy in right parietal edges and increased diffusivity in edges between left thalamus and left parietal nodes. Compared to controls, patients who were rendered seizure-free showed the largest effect size for decreased anisotropy in the edge connecting the left thalamus and right temporal nodes and increased diffusivity in edges connecting right frontal nodes. As demonstrated by large effect sizes, connectomes with decreased anisotropy (edge between right frontal and left insular nodes) and increased diffusivity (edge between right thalamus and left parietal nodes) were found in patients with persistent seizures compared to patients who became seizure-free. Patients who had persistent seizures showed larger effect sizes in all network metrics than patients who became seizure-free when compared to each other and compared to controls. Furthermore, patients with focal-to-bilateral tonic-clonic seizures (FBTCS, N = 11) had decreased quantitative anisotropy in a bilateral network involving edges between temporal, parietal and frontal nodes with greater effect sizes than those of patients without FBTCS (N = 9). NBS findings between patients and controls indicated that structural network changes are not necessarily a consequence of longstanding refractory epilepsy and instead are present at the time of diagnosis. Computed effect sizes suggest that there may be structural network MRI-markers of future pharmacoresistance and seizure severity in patients with a new diagnosis of focal epilepsy.