The aim of this study was to employ artificial intelligence (AI)-based magnetic resonance imaging (MRI) brain volumetry to potentially distinguish between idiopathic normal pressure hydrocephalus (iNPH), Alzheimer\'s disease (AD), and age- and sex-matched healthy controls (CG) by evaluating cortical, subcortical, and ventricular volumes. Additionally, correlations between the measured brain and ventricle volumes and two established semi-quantitative radiologic markers for iNPH were examined. An IRB-approved retrospective analysis was conducted on 123 age- and sex-matched subjects (41 iNPH, 41 AD, and 41 controls), with all of the iNPH patients undergoing routine clinical brain MRI prior to ventriculoperitoneal shunt implantation. Automated AI-based determination of different cortical and subcortical brain and ventricular volumes in mL, as well as calculation of population-based normalized percentiles according to an embedded database, was performed; the CE-certified software mdbrain v4.4.1 or above was used with a standardized T1-weighted 3D magnetization-prepared rapid gradient echo (MPRAGE) sequence. Measured brain volumes and percentiles were analyzed for between-group differences and correlated with semi-quantitative measurements of the Evans\' index and corpus callosal angle: iNPH patients exhibited ventricular enlargement and changes in gray and white matter compared to AD patients and controls, with the most significant differences observed in total ventricular volume (+67%) and the lateral (+68%), third (+38%), and fourth (+31%) ventricles compared to controls. Global ventriculomegaly and marked white matter reduction with concomitant preservation of gray matter compared to AD and CG were characteristic of iNPH, whereas global and frontoparietally accentuated gray matter reductions were characteristic of AD. Evans\' index and corpus callosal angle differed significantly between the three groups and moderately correlated with the lateral ventricular volumes in iNPH patients [Evans\' index (r > 0.83, p ≤ 0.001), corpus callosal angle (r < -0.74, p ≤ 0.001)]. AI-based MRI volumetry in iNPH patients revealed global ventricular enlargement and focal brain atrophy, which, in contrast to healthy controls and AD patients, primarily involved the supratentorial white matter and was marked temporomesially and in the midbrain, while largely preserving gray matter. Integrating AI volumetry in conjunction with traditional radiologic measures could enhance iNPH identification and differentiation, potentially improving patient management and therapy response assessment.

OBJECTIVE: Epilepsy patients are often grouped together by clinical variables. Quantitative neuroimaging metrics can provide a data-driven alternative for grouping of patients. In this work, we leverage ultra-high-field 7-T structural magnetic resonance imaging (MRI) to characterize volumetric atrophy patterns across hippocampal subfields and thalamic nuclei in drug-resistant focal epilepsy.
METHODS: Forty-two drug-resistant epilepsy patients and 13 controls with 7-T structural neuroimaging were included in this study. We measured hippocampal subfield and thalamic nuclei volumetry, and applied an unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), to estimate atrophy patterns across the hippocampal subfields and thalamic nuclei of patients. We studied the association between predefined clinical groups and the estimated atrophy patterns. Additionally, we used hierarchical clustering on the LDA factors to group patients in a data-driven approach.
RESULTS: In patients with mesial temporal sclerosis (MTS), we found a significant decrease in volume across all ipsilateral hippocampal subfields (false discovery rate-corrected p [pFDR] < .01) as well as in some ipsilateral (pFDR < .05) and contralateral (pFDR < .01) thalamic nuclei. In left temporal lobe epilepsy (L-TLE) we saw ipsilateral hippocampal and some bilateral thalamic atrophy (pFDR < .05), whereas in right temporal lobe epilepsy (R-TLE) extensive bilateral hippocampal and thalamic atrophy was observed (pFDR < .05). Atrophy factors demonstrated that our MTS cohort had two atrophy phenotypes: one that affected the ipsilateral hippocampus and one that affected the ipsilateral hippocampus and bilateral anterior thalamus. Atrophy factors demonstrated posterior thalamic atrophy in R-TLE, whereas an anterior thalamic atrophy pattern was more common in L-TLE. Finally, hierarchical clustering of atrophy patterns recapitulated clusters with homogeneous clinical properties.
CONCLUSIONS: Leveraging 7-T MRI, we demonstrate widespread hippocampal and thalamic atrophy in epilepsy. Through unsupervised machine learning, we demonstrate patterns of volumetric atrophy that vary depending on disease subtype. Incorporating these atrophy patterns into clinical practice could help better stratify patients to surgical treatments and specific device implantation strategies.

Two clinical phenotypes characterize the onset of amyotrophic lateral sclerosis (ALS): the spinal variant, with symptoms beginning in the limbs, and the bulbar variant, affecting firstly speech and swallowing. The two variants show some distinct features in the histopathology, localization and prognosis, but to which extent they really differ clinically and pathologically remains to be clarified. Recent neuropathological and neuroimaging studies have suggested a broader spreading of the neurodegenerative process in ALS, extending beyond the motor areas, toward other cortical and deep grey matter regions, many of which are involved in visual processing and saccadic control. Indeed, a wide range of eye movement deficits have been reported in ALS, but they have never been used to distinguish the two ALS variants. Since quantifying eye movements is a very sensitive and specific method for the study of brain networks, we compared different saccadic and visual search behaviours across spinal ALS patients (n = 12), bulbar ALS patients (n = 6) and healthy control subjects (n = 13), along with cognitive and MRI measures, with the aim to define more accurately the two patients subgroups and possibly clarify a different underlying neural impairment. We found separate profiles of visually-guided saccades between spinal (short saccades) and bulbar (slow saccades) ALS, which could result from the pathologic involvement of different pathways. We suggest an early involvement of the parieto-collicular-cerebellar network in spinal ALS and the fronto-brainstem circuit in bulbar ALS. Overall, our data confirm the diagnostic value of the eye movements analysis in ALS and add new insight on the involved neural networks.

We collected neonatal neurological, clinical, and imaging data to study the neurological manifestations and imaging characteristics of neonates with coronavirus disease 2019 (COVID-19).
This case-control study included newborns diagnosed with COVID-19 in Wuhan, China from January 2020 to July 2020. All included newborns had complete neurological evaluations and head magnetic resonance imaging. We normalized the extracted T2-weighted imaging data to a standard neonate template space, and segmented them into gray matter, white matter, and cerebrospinal fluid. The comparison of gray matter volume was conducted between the two groups.
A total of five neonates with COVID-19 were included in this study. The median reflex scores were 2 points lower in the infected group than in the control group (P = 0.0094), and the median orientation and behavior scores were 2.5 points lower in the infected group than in the control group (P = 0.0008). There were also significant differences between the two groups in the total scale score (P = 0.0426). The caudate nucleus, parahippocampal gyrus, and thalamus had the strongest correlations with the Hammersmith neonatal neurologic examination (HNNE) score, and the absolute correlation coefficients between the gray matter volumes and each part of the HNNE score were all almost greater than 0.5.
We first compared the neurological performance of neonates with and without COVID-19 by quantitative neuroimaging and neurological examination methods. Considering the limited numbers of patients, more studies focusing on the structural or functional aspects of the virus in the central nervous system in different age groups will be carried out in the future.

Automated neuroimaging methods like FreeSurfer ( https://surfer.nmr.mgh.harvard.edu/ ) have revolutionized quantitative neuroimaging analyses. Such analyses provide a variety of metrics used for image quantification, including magnetic resonance imaging (MRI) volumetrics. With the release of FreeSurfer version 6.0, it is important to assess its comparability to the widely-used previous version 5.3. The current study used data from the initial 249 participants in the ongoing Chronic Effects of Neurotrauma Consortium (CENC) multicenter observational study to compare the volumetric output of versions 5.3 and 6.0 across various regions of interest (ROI). In the current investigation, the following ROIs were examined: total intracranial volume, total white matter volume, total ventricular volume, total gray matter volume, and right and left volumes for the thalamus, pallidum, putamen, caudate, amygdala and hippocampus. Absolute ROI volumes derived from FreeSurfer 6.0 differed significantly from those obtained using version 5.3. We also employed a clinically-based evaluation strategy to compare both versions in their prediction of age-mediated volume reductions (or ventricular increase) in the aforementioned structures. Statistical comparison involved both general linear modeling (GLM) and random forest (RF) methods, where cross-validation error was significantly higher using segmentations from FreeSurfer version 5.3 versus version 6.0 (GLM: t = 4.97, df = 99, p value = 2.706e-06; RF: t = 4.85, df = 99, p value = 4.424e-06). Additionally, the relative importance of ROIs used to predict age using RFs differed between FreeSurfer versions, indicating substantial differences in the two versions. However, from the perspective of correlational analyses, fitted regression lines and their slopes were similar between the two versions, regardless of version used. While absolute volumes are not interchangeable between version 5.3 and 6.0, ROI correlational analyses appear to yield similar results, suggesting the interchangeability of ROI volume for correlational studies.

To reveal the immediate extent of trauma-induced neurodegenerative changes rostral to the level of lesion and determine the predictive clinical value of quantitative MRI (qMRI) following acute spinal cord injury (SCI).
Twenty-four acute SCI patients and 23 healthy controls underwent a high-resolution T1-weighted protocol. Eighteen of those patients and 20 of controls additionally underwent a multi-parameter mapping (MPM) MRI protocol sensitive to the content of tissue structure, including myelin and iron. Patients were examined clinically at baseline, 2, 6, 12, and 24 months post-SCI. We assessed volume and microstructural changes in the spinal cord and brain using T1-weighted MRI, magnetization transfer (MT), longitudinal relaxation rate (R1), and effective transverse relaxation rate (R2*) maps. Regression analysis determined associations between acute qMRI parameters and recovery.
At baseline, cord area and its anterior-posterior width were decreased in patients, whereas MT, R1, and R2* parameters remained unchanged in the cord. Within the cerebellum, volume decrease was paralleled by increases of MT and R2* parameters. Early grey matter changes were observed within the primary motor cortex and limbic system. Importantly, early volume and microstructural changes of the cord and cerebellum predicted functional recovery following injury.
Neurodegenerative changes rostral to the level of lesion occur early in SCI, with varying temporal and spatial dynamics. Early qMRI markers of spinal cord and cerebellum are predictive of functional recovery. These neuroimaging biomarkers may supplement clinical assessments and provide insights into the potential of therapeutic interventions to enhance neural plasticity.

Structural neuroimaging of athletes who have sustained a sports-related concussion (SRC) can be viewed as either standard clinical imaging or with advanced neuroimaging methods that quantitatively assess brain structure. Negative findings from conventional computed tomography (CT) or magnetic resonance imaging (MRI) are the norm in SRC. Nonetheless, these conventional measures remain the first line of neuroimaging of the athlete as they do detect clinically significant pathologies, when present, such as hemorrhagic abnormalities in the form of hematomas, contusions and mircobleeds along with regions of focal encephalomalacia or other signal abnormalities, with CT best capable of detecting skull fractures. However, advanced neuroimaging techniques hold particular promise in detecting subtle neuropathology in the athlete which standard clinical neuroimaging cannot. To best understand what conventional as well as quantitative neuroimaging methods are detecting in SRC, this review begins by covering basic neuroanatomical principles associated with mild traumatic brain injury (mTBI) and the brain regions most vulnerable to injury from SRC, as these regions define where advanced neuroimaging methods most likely detect abnormalities. Advanced MRI techniques incorporate quantitative metrics that include volume, shape, thickness along with diffusion parameters that provide a more fine-grained analysis of brain structure. With advancements in image analysis, multiple quantitative neuroimaging metrics now can be utilized in assessing SRC. Such multimodality approaches are particularly relevant and important for assessing white matter and network integrity of the brain following injury, including SRC. This review focuses just on the structural side of neuroimaging in SRC, but these techniques also are being integrated with functional neuroimaging, where the combination of the two approaches may provide superior methods in assessing the pathological effects of SRC.

Meditation and spiritual practices are conceptually similar, eliciting similar subjective experiences, and both appear to provide similar benefits to the practicing individuals. However, no research has examined whether the mechanism of action leading to the beneficial effects is similar in both practices. This review examines the neuroimaging research that has focused on groups of meditating individuals, groups who engage in religious/spiritual practices, and research that has examined groups who perform both practices together, in an attempt to assess whether this may be the case. Differences in the balance of activity between the parietal and prefrontal cortical activation were found between the three groups. A relative prefrontal increase was reflective of mindfulness, which related to decreased anxiety and improved well-being. A relative decrease in activation of the parietal cortex, specifically the inferior parietal cortex, appears to be reflective of spiritual belief, whether within the context of meditation or not. Because mindful and spiritual practices differ in focus regarding the \'self\' or \'other\' (higher being), these observations about neurological components that reflect spirituality may continue work towards understanding how the definition of \'self\' and \'other\' is represented in the brain, and how this may be reflected in behaviour. Future research can begin to use cohorts of participants in mindfulness studies which are controlled for using the variable of spirituality to explicitly examine how functional and structural similarities and differences may arise.

Mesial temporal lobe epilepsy (MTLE) is the most common of the surgically remediable drug-resistant epilepsies. MRI is the primary diagnostic tool to detect anatomical abnormalities and, when combined with EEG, can more accurately identify an epileptogenic lesion, which is often hippocampal sclerosis in cases of MTLE. As structural imaging technology has advanced the surgical treatment of MTLE and other lesional epilepsies, so too have the analysis techniques that are used to measure different structural attributes of the brain. These techniques, which are reviewed here and have been used chiefly in basic research of epilepsy and in studies of MTLE, have identified different types and the extent of anatomical abnormalities that can extend beyond the affected hippocampus. These results suggest that structural imaging and sophisticated imaging analysis could provide important information to identify networks capable of generating spontaneous seizures and ultimately help guide surgical therapy that improves postsurgical seizure-freedom outcomes.