MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer's disease, and healthy controls

BackgroundRadiological 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.MethodWe 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.ResultsWe 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 ConclusionsAI (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)

A genome-wide association study in autoimmune neurological syndromes with anti-GAD65 autoantibodies

Strippel C, Herrera-Rivero M, Wendorff M, et al. A genome-wide association study in autoimmune neurological syndromes with anti-GAD65 autoantibodies. Brain: A Journal of Neurology . 2022: awac119.Autoimmune neurological syndromes (AINS) with autoantibodies against the 65  kDa isoform of the glutamic acid decarboxylase (GAD65) present with limbic encephalitis including temporal lobe seizures or epilepsy, cerebellitis with ataxia, and stiff-person-syndrome, or overlap forms. Anti-GAD65 autoantibodies are also detected in autoimmune diabetes mellitus, which has a strong genetic susceptibility conferred by human leukocyte antigen (HLA) and non-HLA genomic regions. We investigated the genetic predisposition in patients with anti-GAD65 AINS. We performed a genome-wide association study (GWAS) and an association analysis of the HLA region in a large German cohort of 1,214 individuals. These included 167 patients with anti-GAD65 AINS, recruited by the German Network for Research on Autoimmune Encephalitis (GENERATE), and 1,047 individuals without neurological or endocrine disease as population-based controls. Predictions of protein expression changes based on GWAS findings were further explored and validated in the CSF proteome of a virtually independent cohort of 10 patients with GAD65-AINS and 10 controls. Our GWAS identified 16 genome-wide significant (p90%) mapped to non-coding regions of the genome. Over 40% of the variants have known regulatory functions on the expression of 48 genes in disease relevant cells and tissues, mainly CD4+ T cells and the cerebral cortex. The annotation of epigenomic marks suggested specificity for neural and immune cells. A network analysis of the implicated protein-coding genes highlighted the role of protein kinase C beta (PRKCB) and identified an enrichment of numerous biological pathways participating in immunity and neural function. Analysis of the classical HLA alleles and haplotypes showed no genome-wide significant associations. The strongest associations were found for the DQA1*03:01-DQB1*03:02-DRB1*04:01HLA haplotype (p=4.39*10-4, OR=2.5, 95%CI= 1.499-4.157), and DRB1*04:01 allele (p=8.3*10-5, OR=2.4, 95%CI=1.548-3.682) identified in our cohort. As predicted, the CSF proteome showed differential levels of five proteins (HLA-A/B, C4A, ATG4D and NEO1) of eQTL genes from our GWAS in the CSF proteome of anti-GAD65 AINS. These findings suggest a strong genetic predisposition with direct functional implications for immunity and neural function in anti-GAD65 AINS, mainly conferred by genomic regions outside the classical HLA alleles. © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain