Background Radiological identification of temporal lobe epilepsy (TLE) is crucial for diagnosis and treatment planning. TLE neuroimaging abnormalities are pervasive at the group level, but they can be subtle and difficult to identify by visual inspection of individual scans, prompting applications of artificial intelligence (AI) assisted technologies. Method We assessed the ability of a convolutional neural network (CNN) algorithm to classify TLE vs. patients with AD vs. healthy controls using T1-weighted magnetic resonance imaging (MRI) scans. We used feature visualization techniques to identify regions the CNN employed to differentiate disease types. Results We show the following classification results: healthy control accuracy = 81.54% (SD = 1.77%), precision = 0.81 (SD = 0.02), recall = 0.85 (SD = 0.03), and F1-score = 0.83 (SD = 0.02); TLE accuracy = 90.45% (SD = 1.59%), precision = 0.86 (SD = 0.03), recall = 0.86 (SD = 0.04), and F1-score = 0.85 (SD = 0.04); and AD accuracy = 88.52% (SD = 1.27%), precision = 0.64 (SD = 0.05), recall = 0.53 (SD = 0.07), and F1 score = 0.58 (0.05). The high accuracy in identification of TLE was remarkable, considering that only 47% of the cohort had deemed to be lesional based on MRI alone. Model predictions were also considerably better than random permutation classifications (p < 0.01) and were independent of age effects. Conclusions AI (CNN deep learning) can classify and distinguish TLE, underscoring its potential utility for future computer-aided radiological assessments of epilepsy, especially for patients who do not exhibit easily identifiable TLE associated MRI features (e.g., hippocampal sclerosis).

Multilineage-differentiating stress-enduring (Muse) cells were discovered in 2010 as a subpopulation of mesenchymal stroma cells (MSCs). Muse cells can self-renew and tolerate severe culturing conditions. These cells can differentiate into three lineage cells spontaneously or in induced medium but do not form teratoma in vitro or in vivo. Central nervous system (CNS) diseases, such as intracerebral hemorrhage (ICH), cerebral infarction, and spinal cord injury are normally disastrous. Despite numerous therapy strategies, CNS diseases are difficult to recover. As a novel kind of pluripotent stem cells, Muse cells have shown great regeneration capacity in many animal models, including acute myocardial infarction, hepatectomy, and acute cerebral ischemia (ACI). After injection into injury sites, Muse cells survived, migrated, and differentiated into functional neurons with synaptic junctions to local neurons and contributed to recovery of function. Furthermore, Muse cell differentiation did not need to be induced pre-transplantation and no tumors were observed post- transplantation. The Muse cell population is promising and may lead to a revolution in regenerative medicine. This review focuses on recent advances regarding the Muse cells therapies in Neurorestoratology and discusses future perspectives in this field.

Background and Objectives:A new frontier in diagnostic radiology is the inclusion of machine-assisted support tools that facilitate the identification of subtle lesions often not visible to the human eye. Structural neuroimaging plays an essential role in the identification of lesions in patients with epilepsy, which often coincide with the seizure focus. Here we explore the potential for a convolutional neural network (CNN) to determine lateralization of seizure onset in patients with epilepsy using T1-weighted structural MRI scans as input.Methods:Using a dataset of 359 patients with temporal lobe epilepsy (TLE) from 7 surgical centers, we tested whether a CNN based on T1-weighted images could classify seizure laterality concordant with clinical team consensus. This CNN was compared to a randomized model (comparison to chance) and a hippocampal volume logistic regression (comparison to current clinically-available measures). Furthermore, we leveraged a CNN feature visualization technique to identify regions used to classify patients.Results:Across 100 runs, the CNN model was concordant with clinician lateralization on average 78% (SD= 5.1%) of runs with the best performing model achieving 89% concordance. The CNN outperformed the randomized model (average concordance of 51.7%) on 100% of runs with an average improvement of 26.2% and outperformed the hippocampal volume model (average concordance of 71.7%) on 85% of runs with an average improvement of 6.25%. Feature visualization maps revealed that in addition to the medial temporal lobe, regions in the lateral temporal lobe, cingulate, and precentral gyrus aided in classification.Discussion:These extra-temporal lobe features underscore the importance of whole-brain models to highlight areas worthy of clinician scrutiny during temporal lobe epilepsy lateralization. This proof-of-concept study illustrates that a CNN applied to structural MRI data can visually aid clinician-led localization of epileptogenic zone and identify extra-hippocampal regions that may require additional radiological attention.Classification of Evidence:This study provides Class II evidence that in patients with drug-resistant unilateral temporal lobe epilepsy, a convolutional neural network algorithm derived from T1-weighted MR images can correctly classify seizure laterality.

Objectives To investigate the hypothesis that language recovery in post‐stroke aphasia is associated with structural brain changes. Methods We evaluated whether treatment‐induced improvement in naming is associated with reorganization of tissue microstructure within residual cortical regions. To this end, we performed a retrospective longitudinal treatment study using comprehensive language‐linguistic assessments and diffusion MRI sequences optimized for the assessment of complex microstructure (diffusional kurtosis imaging) to evaluate the relationship between language treatment response and cortical changes in 26 individuals with chronic stroke‐induced aphasia. We employed elastic net statistical models controlling for baseline factors including age, sex, and time since the stroke, as well as lesion volume. Results We observed that improved naming accuracy (Philadelphia Naming Test) was statistically associated with increased post‐treatment microstructural integrity in the left posterior superior temporal gyrus. Moreover, increase in microstructural integrity in the left middle temporal gyrus and left inferior temporal gyrus was specifically associated with a decrease in semantic paraphasias. This longitudinal relationship between brain tissue integrity and language improvement was not observed in other non‐language related brain regions. Interpretation Our findings provide evidence that structural brain changes in the preserved left hemisphere regions are associated with treatment‐induced language recovery in aphasia and are part of the mechanisms supporting language and brain injury recovery.

Purpose: Object naming requires visual decoding, conceptualization, semantic categorization, and phonological encoding, all within 400 to 600 ms of stimulus presentation and before a word is spoken. In this study, we sought to predict semantic categories of naming responses based on prearticulatory brain activity recorded with scalp EEG in healthy individuals. Methods:We assessed 19 healthy individuals who completed a naming task while undergoing EEG. The naming task consisted of 120 drawings of animate/inanimate objects or abstract drawings. We applied a one-dimensional, two-layer, neural network to predict the semantic categories of naming responses based on prearticulatory brain activity.Results: Classifications of animate, inanimate, and abstract responses had an average accuracy of 80%, sensitivity of 72%, and specificity of 87% across participants. Across participants, time points with the highest average weights were between 470 and 490 milliseconds after stimulus presentation, and electrodes with the highest weights were located over the left and right frontal brain areas.Conclusions: Scalp EEG can be successfully used in predicting naming responses through prearticulatory brain activity. Interparticipant variability in feature weights suggests that individualized models are necessary for highest accuracy. Our findings may inform future applications of EEG in reconstructing speech for individuals with and without speech impairments.