Diffusion Weighted Imaging (DWI), which is based on Echo Planar Imaging (EPI) protocols, is becoming increasingly important for neurosurgical applications. However, its use in this context is limited ...in part by significant spatial distortion inherent to EPI.
We evaluated an efficient algorithm for EPI distortion correction (EPIC) across 814 DWI scans from 250 brain tumor patients and quantified the magnitude of geometric distortion for whole brain and multiple brain regions.
Evaluation of the algorithm's performance revealed significantly higher mutual information between T1-weighted pre-contrast images and corrected b = 0 images than the uncorrected b = 0 images (p < 0.001). The distortion magnitude across all voxels revealed a median EPI distortion effect of 2.1 mm, ranging from 1.2 mm to 5.9 mm, the 5th and 95th percentile, respectively. Regions adjacent to bone-air interfaces, such as the orbitofrontal cortex, temporal poles, and brain stem, were the regions most severely affected by DWI distortion.
Using EPIC to estimate the degree of distortion in 814 DWI brain tumor images enabled the creation of a topographic atlas of DWI distortion across the brain. The degree of displacement of tumors boundaries in uncorrected images is severe but can be corrected for using EPIC. Our results support the use of distortion correction to ensure accurate and careful application of DWI to neurosurgical practice.
Standard treatment of primary and metastatic brain tumours includes high-dose megavoltage-range radiation to the cranial vault. About half of patients survive >6 months, and many attain long-term ...control or cure. However, 50-90% of survivors exhibit disabling cognitive dysfunction. The radiation-associated cognitive syndrome is poorly understood and has no effective prevention or long-term treatment. Attention has primarily focused on mechanisms of disability that appear at 6 months to 1 year after radiotherapy. However, recent studies show that CNS alterations and dysfunction develop much earlier following radiation exposure. This finding has prompted the hypothesis that subtle early forms of radiation-induced CNS damage could drive chronic pathophysiological processes that lead to permanent cognitive decline. This Review presents evidence of acute radiation-triggered CNS inflammation, injury to neuronal lineages, accessory cells and their progenitors, and loss of supporting structure integrity. Moreover, injury-related processes initiated soon after irradiation could synergistically alter the signalling microenvironment in progenitor cell niches in the brain and the hippocampus, which is a structure critical to memory and cognition. Progenitor cell niche degradation could cause progressive neuronal loss and cognitive disability. The concluding discussion addresses future directions and potential early treatments that might reverse degenerative processes before they can cause permanent cognitive disability.
Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these ...molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.
The Alzheimer's Disease Neuroimaging Initiative (ADNI) separates “early” and “late” mild cognitive impairment (MCI) based on a single memory test. We compared ADNI's MCI classifications to our ...neuropsychological approach, which more broadly assesses cognitive abilities.
Three hundred thirty-six ADNI-2 participants were classified as “early” or “late” MCI. Cluster analysis was performed on neuropsychological test data, and participants were reclassified based on cluster results. These two staging approaches were compared on progression rates, cerebrospinal fluid biomarkers, and cortical thickness profiles.
There was little correspondence between the two staging methods. ADNI's early MCI group included a large proportion of false-positive diagnostic errors. The reclassified neuropsychological MCI groups showed steeper survival curves and more abnormal biomarkers.
Our novel neuropsychological approach improved the staging of MCI by (1) capturing individuals at an early symptomatic stage, (2) minimizing false-positive cases, and (3) identifying a late MCI group further along the disease trajectory.
Cognitive and behavioural comorbidities are prevalent in childhood and adult epilepsies and impose a substantial human and economic burden. Over the past century, the classic approach to ...understanding the aetiology and course of these comorbidities has been through the prism of the medical taxonomy of epilepsy, including its causes, course, characteristics and syndromes. Although this 'lesion model' has long served as the organizing paradigm for the field, substantial challenges to this model have accumulated from diverse sources, including neuroimaging, neuropathology, neuropsychology and network science. Advances in patient stratification and phenotyping point towards a new taxonomy for the cognitive and behavioural comorbidities of epilepsy, which reflects the heterogeneity of their clinical presentation and raises the possibility of a precision medicine approach. As we discuss in this Review, these advances are informing the development of a revised aetiological paradigm that incorporates sophisticated neurobiological measures, genomics, comorbid disease, diversity and adversity, and resilience factors. We describe modifiable risk factors that could guide early identification, treatment and, ultimately, prevention of cognitive and broader neurobehavioural comorbidities in epilepsy and propose a road map to guide future research.
To identify distinct cognitive phenotypes in temporal lobe epilepsy (TLE) and evaluate patterns of white matter (WM) network alterations associated with each phenotype.
Seventy patients with TLE were ...characterized into 4 distinct cognitive phenotypes based on patterns of impairment in language and verbal memory measures (language and memory impaired, memory impaired only, language impaired only, no impairment). Diffusion tensor imaging was obtained in all patients and in 46 healthy controls (HC). Fractional anisotropy (FA) and mean diffusivity (MD) of the WM directly beneath neocortex (i.e., superficial WM SWM) and of deep WM tracts associated with memory and language were calculated for each phenotype. Regional and network-based SWM analyses were performed across phenotypes.
The language and memory impaired group and the memory impaired group showed distinct patterns of microstructural abnormalities in SWM relative to HC. In addition, the language and memory impaired group showed widespread alterations in WM tracts and altered global SWM network topology. Patients with isolated language impairment exhibited poor network structure within perisylvian cortex, despite relatively intact global SWM network structure, whereas patients with no impairment appeared similar to HC across all measures.
These findings demonstrate a differential pattern of WM microstructural abnormalities across distinct cognitive phenotypes in TLE that can be appreciated at both the regional and network levels. These findings not only help to unravel the underlying neurobiology associated with cognitive impairment in TLE, but they could also aid in establishing cognitive taxonomies or in the prediction of cognitive course in TLE.
Radiation-induced cognitive deficits may be mediated by tissue damage to cortical regions. Volumetric changes in cortex can be reliably measured using high-resolution magnetic resonance imaging ...(MRI). We used these methods to study the association between radiation therapy (RT) dose and change in cortical thickness in high-grade glioma (HGG) patients.
We performed a voxel-wise analysis of MRI from 15 HGG patients who underwent fractionated partial brain RT. Three-dimensional MRI was acquired pre- and 1 year post RT. Cortex was parceled with well-validated segmentation software. Surgical cavities were censored. Each cortical voxel was assigned a change in cortical thickness between time points, RT dose value, and neuroanatomic label by lobe. Effects of dose, neuroanatomic location, age, and chemotherapy on cortical thickness were tested using linear mixed effects (LME) modeling.
Cortical atrophy was seen after 1 year post RT with greater effects at higher doses. Estimates from LME modeling showed that cortical thickness decreased by -0.0033 mm (P<.001) for every 1-Gy increase in RT dose. Temporal and limbic cortex exhibited the largest changes in cortical thickness per Gy compared to that in other regions (P<.001). Age and chemotherapy were not significantly associated with change in cortical thickness.
We found dose-dependent thinning of the cerebral cortex, with varying neuroanatomical regional sensitivity, 1 year after fractionated partial brain RT. The magnitude of thinning parallels 1-year atrophy rates seen in neurodegenerative diseases and may contribute to cognitive decline following high-dose RT.
Abstract Background We assessed whether mild cognitive impairment (MCI) subtypes could be empirically derived within the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI cohort and examined ...associated biomarkers and clinical outcomes. Methods Cluster analysis was performed on neuropsychological data from 825 MCI ADNI participants. Results Four subtypes emerged: (1) dysnomic (n = 153), (2) dysexecutive (n = 102), (3) amnestic (n = 288), and (4) cluster-derived normal (n = 282) who performed within normal limits on cognitive testing. The cluster-derived normal group had significantly fewer APOE ε4 carriers and fewer who progressed to dementia compared with the other subtypes; they also evidenced cerebrospinal fluid Alzheimer's disease biomarker profiles that did not differ from the normative reference group. Conclusions Identification of empirically derived MCI subtypes demonstrates heterogeneity in MCI cognitive profiles that is not captured by conventional criteria. The large cluster-derived normal group suggests that conventional diagnostic criteria are susceptible to false-positive errors, with the result that prior MCI studies may be diluting important biomarker relationships.
Epilepsy is a common neurological disorder associated with alterations in cortical and subcortical brain networks. Despite a historical focus on gray matter regions involved in seizure generation and ...propagation, the role of white matter (WM) network disruption in epilepsy and its comorbidities has sparked recent attention. In this review, we describe patterns of WM alterations observed in focal and generalized epilepsy syndromes and highlight studies linking WM disruption to cognitive and psychiatric comorbidities, drug resistance, and poor surgical outcomes. Both tract-based and connectome-based approaches implicate the importance of extratemporal and temporo-limbic WM disconnection across a range of comorbidities, and an evolving literature reveals the utility of WM patterns for predicting outcomes following epilepsy surgery. We encourage new research employing advanced analytic techniques (e.g., machine learning) that will further shape our understanding of epilepsy as a network disorder and guide individualized treatment decisions. We also address the need for research that examines how neuromodulation and other treatments (e.g., laser ablation) affect WM networks, as well as research that leverages larger and more diverse samples, longitudinal designs, and improved magnetic resonance imaging acquisitions. These steps will be critical to ensuring generalizability of current research and determining the extent to which neuroplasticity within WM networks can influence patient outcomes.
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