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Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. [ABSTRACT FROM AUTHOR]

Proper topographically organized neural connections between the thalamus and the cerebral cortex are mandatory for thalamus function. Thalamocortical (TC) fiber growth begins during the embryonic period and completes by the third trimester of gestation, so that human neonates at birth have a thalamus with a near-facsimile of adult functional parcellation. Whether congenital neocortical anomaly (e.g., lissencephaly) affects TC connection in humans is unknown. Here, via diffusion MRI fiber-tractography analysis of long-term formalin-fixed postmortem fetal brain diagnosed as lissencephaly in comparison with an age-matched normal one, we found similar topological patterns of thalamic subregions and of internal capsule parcellated by TC fibers. However, lissencephaly fetal brain showed white matter structural changes, including fewer/less organized TC fibers and optic radiations, and much less cortical plate invasion by TC fibers — particularly around the shallow central sulcus. Diffusion MRI fiber tractography of normal fetal brains at 15, 23, and 26 gestational weeks (GW) revealed dynamic volumetric change of each parcellated thalamic subregion, suggesting coupled developmental progress of the thalamus with the corresponding cortex. Moreover, from GW23 and GW26 normal fetal brains, TC endings in the cortical plate could be delineated to reflect cumulative progressive TC invasion of cortical plate. By contrast, lissencephaly brain showed a dramatic decrease in TC invasion of the cortical plate. Our study thus shows the feasibility of diffusion MRI fiber tractography in postmortem long-term formalin-fixed fetal brains to disclose the developmental progress of TC tracts coordinating with thalamic and neocortical growth both in normal and lissencephaly fetal brains at mid-gestational stage.

Parkinson's disease (PD) is the second most common age-related neurodegenerative disease with cardinal motor symptoms. In addition to motor symptoms, PD is a heterogeneous disease accompanied by many non-motor symptoms that dominate the clinical manifestations in different stages or subtypes of PD, such as cognitive impairments. The heterogeneity of PD suggests widespread brain structural changes, and axonal involvement appears to be critical to the pathophysiology of PD. As α-synuclein pathology has been suggested to cause axonal changes followed by neuronal degeneration, diffusion tensor imaging (DTI) as an in vivo imaging technique emerges to characterize early detectable white matter changes due to PD. Here, we reviewed the past 5-year literature to show how DTI has helped identify axonal abnormalities at different PD stages or in different PD subtypes and atypical parkinsonism. We also showed the recent clinical utilities of DTI tractography in interventional treatments such as deep brain stimulation (DBS). Mounting evidence supported by multisite DTI data suggests that DTI along with the advanced analytic methods, can delineate dynamic pathophysiological processes from the early to late PD stages and differentiate distinct structural networks affected in PD and other parkinsonism syndromes. It indicates that DTI, along with recent advanced analytic methods, can assist future interventional studies in optimizing treatments for PD patients with different clinical conditions and risk profiles. [ABSTRACT FROM AUTHOR]

Objective: To determine the structural networks that constrain propagation of ictal oscillations during epileptic spasm events, and compare the observed propagation patterns across patients with successful or unsuccessful surgical outcomes. Methods: Subdural electrode recordings of 18 young patients (age 1–11 years) were analyzed during epileptic spasm events to determine ictal networks and quantify the amplitude and onset time of ictal oscillations across the cortical surface. Corresponding structural networks were generated with diffusion magnetic resonance imaging (MRI) tractography by seeding the cortical region associated with the earliest average oscillation onset time, and white matter pathways connecting active electrode regions within the ictal network were isolated. Properties of this structural network were used to predict oscillation onset times and amplitudes, and this relationship was compared across patients who did and did not achieve seizure freedom following resective surgery. Results: Onset propagation patterns were relatively consistent across each patient's spasm events. An electrode's average ictal oscillation onset latency was most significantly associated with the length of direct corticocortical tracts connecting to the area with the earliest average oscillation onset (p <.001, model R2 =.54). Moreover, patients demonstrating a faster propagation of ictal oscillation signals within the corticocortical network were more likely to have seizure recurrence following resective surgery (p =.039). In addition, ictal oscillation amplitude was associated with connecting tractography length and weighted fractional anisotropy (FA) measures along these pathways (p =.002/.030, model R2 =.31/.25). Characteristics of analogous corticothalamic pathways did not show significant associations with ictal oscillation onset latency or amplitude. Significance: Spatiotemporal propagation patterns of high‐frequency activity in epileptic spasms align with length and FA measures from onset‐originating corticocortical pathways. Considering the data in this individualized framework may help inform surgical decision‐making and expectations of surgical outcomes. [ABSTRACT FROM AUTHOR]

Internalizing symptoms typically emerge in adolescence and are more prevalent in females than in males; in contrast, externalizing symptoms typically emerge in childhood and are more commonly observed in males. Previous research has implicated aspects of white matter organization, including fractional anisotropy (FA), of cerebral tracts in both internalizing and externalizing symptoms. Although the cerebellum has been posited to integrate limbic and cortical regions, its role in psychopathology is not well understood. In this longitudinal study, we investigated whether FA of the superior (SCP), middle (MCP), and inferior cerebellar peduncles (ICP) predict change in symptoms and whether sex moderates this association. One hundred eleven adolescents completed the Youth Self-Report, assessing symptoms at baseline (ages 9–13 years) and again 2 years later. Participants also underwent diffusion-weighted imaging at baseline. We used deterministic tractography to segment and compute mean FA of the cerebellar peduncles. Lower FA of the right SCP at baseline predicted increases in internalizing symptoms in females only. Lower FA in the right SCP and ICP also predicted increases in externalizing symptoms in females, but these associations did not survive multiple comparison correction. There was no association between FA of the cerebellar peduncles and change in symptoms in males or between MCP FA and symptom changes in males or females. Organizational properties of the SCP may be a sex-specific marker of internalizing symptom changes in adolescence. The cerebellar peduncles should be explored further in future studies to elucidate sex differences in symptoms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Parkinson’s disease affects at least 10 million people worldwide. It is a neurodegenerative disease which is currently diagnosed by neurological examination. No neuroimaging investigation or blood biomarker is available to aid diagnosis and prognosis. Most effort toward diagnosis using magnetic resonance has been focused on the use of structural/anatomical neuroimaging and diffusion tensor imaging. However, deep brain stimulation, a current strategy for treating Parkinson’s disease, is guided by magnetic resonance imaging. For clinical prognosis, diagnosis and follow-up investigations, blood oxygen level–dependent magnetic resonance imaging, diffusion tensor imaging, spectroscopy and transcranial magnetic stimulation have been used. These techniques represent the state of the art in the last five years. Here, we focus on magnetic resonance techniques for the diagnosis and treatment of Parkinson’s disease.

In this study we wished to examine the relationship between the structural connectivity of the fornix, a white matter (WM) tract in the limbic system which is affected in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD), and the resting state functional connectivity (FC) of two key related subcortical structures, the thalamus and hippocampus. Twenty-two older healthy controls (HC) and 18 older adults with aMCI underwent multi-modal MRI scanning. The fornix was reconstructed using constrained-spherical deconvolution (CSD)-based tractography. The FC between the thalamus and hippocampus was calculated using a region-of-interest approach from which the mean time series were exacted and correlated. Diffusion tensor imaging (DTI) measures of the white matter microstructure of the fornix were correlated against the Fisher Z correlation values from the FC analysis. There was no difference between the groups in the fornix white matter measures, nor in the resting state FC of the thalamus and hippocampus. We did however find that the relationship between functional and structural connectivity differed significantly between the groups. In the HCs there was a significant positive association between linear diffusion (CL) in the fornix and the FC of the thalamus and hippocampus, however there was no relationship between these measures in the aMCI group. These preliminary findings suggest that in aMCI, the relationship between the functional and structural connectivity of regions of the limbic system may be significantly altered compared to healthy ageing. The combined use of DWI and fMRI may advance our understanding of neural network changes in aMCI, and elucidate subtle changes in the relationship between structural and functional brain networks.

Neuroimaging and transcranial magnetic stimulation provide insights into the neuronal mechanisms underlying speech disfluencies in chronic persistent stuttering. In the present paper, the goal is not to provide an exhaustive review of existing literature, but rather to highlight robust findings. We, therefore, conducted a meta-analysis of diffusion tensor imaging studies which have recently implicated disrupted white matter connectivity in stuttering. A reduction of fractional anisotropy in persistent stuttering has been reported at several different loci. Our meta-analysis revealed consistent deficits in the left dorsal stream and in the interhemispheric connections between the sensorimotor cortices. In addition, recent fMRI meta-analyses link stuttering to reduced left fronto-parieto-temporal activation while greater fluency is associated with boosted co-activations of right fronto-parieto-temporal areas. However, the physiological foundation of these irregularities is not accessible with MRI. Complementary, transcranial magnetic stimulation (TMS) reveals local excitatory and inhibitory regulation of cortical dynamics. Applied to a speech motor area, TMS revealed reduced speech-planning-related neuronal dynamics at the level of the primary motor cortex in stuttering. Together, this review provides a focused view of the neurobiology of stuttering to date and may guide the rational design of future research. This future needs to account for the perpetual dynamic interactions between auditory, somatosensory, and speech motor circuits that shape fluent speech. [ABSTRACT FROM AUTHOR]

Communication between the prefrontal cortex and subcortical nuclei underpins the control and inhibition of behavior. However, the interactions in such pathways remain controversial. Using a stop-signal response inhibition task and functional imaging with analysis of effective connectivity, we show that the lateral prefrontal cortex influences the strength of communication between regions in the frontostriatal motor system. We compared 20 generative models that represented alternative interactions between the inferior frontal gyrus, presupplementary motor area (preSMA), subthalamic nucleus (STN), and primary motor cortex during response inhibition. Bayesian model selection revealed that during successful response inhibition, the inferior frontal gyrus modulates an excitatory influence of the preSMA on the STN, thereby amplifying the downstream polysynaptic inhibition from the STN to the motor cortex. Critically, the strength of the interaction between preSMA and STN, and the degree of modulation by the inferior frontal gyrus, predicted individual differences in participants' stopping performance (stop-signal reaction time). We then used diffusion-weighted imaging with tractography to assess white matter structure in the pathways connecting these three regions. The mean diffusivity in tracts between preSMA and the STN, and between the inferior frontal gyrus and STN, also predicted individual differences in stopping efficiency. Finally, we found that white matter structure in the tract between preSMA and STN correlated with effective connectivity of the same pathway, providing important cross-modal validation of the effective connectivity measures. Together, the results demonstrate the network dynamics and modulatory role of the prefrontal cortex that underpin individual differences in inhibitory control. [ABSTRACT FROM AUTHOR]

Objective To examine whether diffusion-weighted imaging ( DWI) tractography can detect multiple white matter pathways connected to language cortices, we employed a maximum a posteriori probability ( MAP) classification method, which has been recently validated for the corticospinal tract. Methods DWI was performed in 12 normally developing children and 17 children with intractable focal epilepsy who underwent subsequent two-stage epilepsy surgery with intracranial functional mapping. First, whole-brain DWI tractography was performed to identify unique pathways originating from Broca's area, premotor area, and Wernicke's area on functional magnetic resonance imaging (f MRI) of normal children and intracranial electrical stimulation mapping (ESM) of children with epilepsy. Group averaging of these pathways based on fMRI was performed to construct the probability maps of language areas in standard MRI space. These maps were finally used to design a DWI-MAP classifier, which can automatically sort individual fibers originating from fMRI language areas as well as ESM language areas. Results In normally developing children, the DWI-MAP classifier predicted language-activation areas on fMRI with up to 77% accuracy. In children with focal epilepsy, the DWI-MAP classifier also showed high accuracy (up to 82%) for the fibers terminating in proximity to essential language areas determined by ESM. Decreased volumes in DWI-MAP-defined pathways after epilepsy surgery were associated with postoperative language deficits. Significance This study encourages further investigations to determine if DWI- MAP analysis can serve as a noninvasive diagnostic tool during pediatric presurgical planning by estimating not only the location of essential language cortices, but also the underlying fibers connecting these cortical areas. [ABSTRACT FROM AUTHOR]

Abstract: Background: TMS activations of white matter depend not only on the distance from the coil, but also on the orientation of the axons relative to the TMS-induced electric field, and especially on axonal bends that create strong local field gradient maxima. Therefore, tractography contains potentially useful information for TMS targeting. Objective/methods: Here, we utilized 1-mm resolution diffusion and structural T1-weighted MRI to construct large-scale tractography models, and localized TMS white matter activations in motor cortex using electromagnetic forward modeling in a boundary element model (BEM). Results: As expected, in sulcal walls, pyramidal cell axonal bends created preferred sites of activation that were not found in gyral crowns. The model agreed with the well-known coil orientation sensitivity of motor cortex, and also suggested unexpected activation distributions emerging from the E-field and tract configurations. We further propose a novel method for computing the optimal coil location and orientation to maximally stimulate a pre-determined axonal bundle. Conclusions: Diffusion MRI tractography with electromagnetic modeling may improve spatial specificity and efficacy of TMS. [Copyright &y& Elsevier]

Purpose As an alternative tool to identify cortical motor areas for planning surgical resection in children with focal epilepsy, the present study proposed a maximum a posteriori probability ( MAP) classification of corticospinal tract ( CST) visualized by diffusion MR tractography. Methods Diffusion-weighted imaging ( DWI) was performed in 17 normally developing children and 20 children with focal epilepsy. An independent component analysis tractography combined with ball-stick model was performed to identify unique CST pathways originating from mouth/lip, finger, and leg areas determined by functional magnetic resonance imaging (f MRI) in healthy children and electrical stimulation mapping ( ESM) in children with epilepsy. Group analyses were performed to construct stereotaxic probability maps of primary motor pathways connecting precentral gyrus and posterior limb of internal capsule, and then utilized to design a novel MAP classifier that can sort individual CST fibers associated with three classes of interest: mouth/lip, fingers, and leg. A systematic leave-one-out approach was applied to train an optimal classifier. A match was considered to occur if classified fibers contacted or surrounded true areas localized by f MRI and ESM. Key Findings It was found that the DWI-MAP provided high accuracy for the CST fibers terminating in proximity to the localization of fMRI/ ESM: 78%/77% for mouth/lip, 77%/76% for fingers, 78%/86% for leg (contact), and 93%/89% for mouth/lip, 91%/89% for fingers, and 92%/88% for leg (surrounded within 2 cm). Significance This study provides preliminary evidence that in the absence of fMRI and ESM data, the DWI-MAP approach can effectively retrieve the locations of cortical motor areas and underlying CST courses for planning epilepsy surgery. [ABSTRACT FROM AUTHOR]