In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or “topics,” provide a sparse summary of the generative process behind the features for each individual. Topic modeling, an unsupervised generative model, has been used to map seemingly disparate features to a common domain. We use Non-Negative Matrix Factorization (NMF) to infer the latent structure of multimodal ADHD data containing fMRI, MRI, phenotypic and behavioral measurements. We compare four different NMF algorithms and find the sparsest decomposition is also the most differentiating between ADHD and healthy patients. We identify dimensions that map to interpretable, recognizable dimensions such as motion, default mode network activity, and other such features of the input data. For example, structural and functional graph theory features related to default mode subnetworks clustered with the ADHD inattentive diagnosis. Structural measurements of the default mode network (DMN) regions such as the posterior cingulate, precuneus, and parahippocampal regions were all related to the ADHD-Inattentive diagnosis. Ventral DMN subnetworks may have more functional connections in ADHD-I, while dorsal DMN may have less. We also find that ADHD topics may be dependent upon diagnostic site, raising the possibility of the diagnostic differences across geographic locations. We assess our findings in light of the ADHD-200 classification competition, and contrast our unsupervised, nominated topics with previously published supervised learning methods. Finally, we demonstrate the validity of these latent variables as biomarkers by using them for classification of ADHD in 730 patients. Cumulatively, this manuscript addresses how multi-modal data in ADHD can be interpreted by latent dimensions.

High classification accuracies in neuroimaging studies of ADHD appear to be inflated by circular analysis and small sample size. Accuracies on independent datasets were consistent with known heterogeneity of the disorder. Steps to resolve these issues, and a shift toward accounting for sample heterogeneity and prediction of future outcomes, will be crucial in future classification studies in ADHD.

Purpose The average delay from first seizure to diagnosis of psychogenic non-epileptic seizures (PNES) is over 7 years. The reason for this delay is not well understood. We hypothesized that a perceived decrease in seizure frequency after starting an anti-seizure medication (ASM) may contribute to longer delays, but the frequency of such a response has not been well established. Methods Time from onset to diagnosis, medication history and associated seizure frequency was acquired from the medical records of 297 consecutive patients with PNES diagnosed using video-electroencephalographic monitoring. Exponential regression was used to model the effect of medication trials and response on diagnostic delay. Results Mean diagnostic delay was 8.4 years (min 1 day, max 52 years). The robust average diagnostic delay was 2.8 years (95% CI: 2.2-3.5 years) based on an exponential model as 10 to the mean of log10delay. Each ASM trial increased the robust average delay exponentially by at least one third of a year (Wald t=3.6, p=0.004). Response to ASM trials did not significantly change diagnostic delay (Wald t=−0.9, p=0.38). Conclusion Although a response to ASMs was observed commonly in these patients with PNES, the presence of a response was not associated with longer time until definitive diagnosis. Instead, the number of ASMs tried was associated with a longer delay until diagnosis, suggesting that ASM trials were continued despite lack of response. These data support the guideline that patients with seizures should be referred to epilepsy care centers after failure of two medication trials.

Restraint elicits a number of physiological stress responses that can be increased or decreased in magnitude based on prior stress history. For instance, repeated exposure to restraint leads to habituation of hypothalamic-pituitary-adrenal (HPA) activation to restraint. In contrast, acute restraint after a different repeated stressor leads to facilitation of HPA activity to the novel stress. Acute restraint also elicits a variety of behaviors including struggling, but the effect of prior stress in regulating behavioral responses to restraint is not clear. The goal of the present studies was to assess struggling during restraint with or without a prior history of repeated stress. Using automated behavioral analysis software (EthoVision), we quantified struggling during restraint. We found that acutely restrained rats exhibit vigorous struggling behavior that declines during a single restraint period. Repeated restraint lead to habituated struggling behavior, whereas acute restraint after repeated swim elicited facilitated struggling behavior. These effects on struggling were found alongside expected differences in HPA activity. Removing stress-induced increases in corticosterone via adrenalectomy did not significantly affect struggling responses to restraint. Overall, restraintinduced struggling appears to be regulated in a manner similar to HPA responses to restraint, but is not dictated by adrenal hormones.

Chemokines acting through G protein-coupled receptors play an essential role in the immune response. PI3K and phospholipase C (PLC) are distinct signaling molecules that have been proposed in the regulation of chemokine-mediated cell migration. Studies with knockout mice have demonstrated a critical role for PI3K in Gαi protein-coupled receptor-mediated neutrophil and lymphocyte chemotaxis. Although PLCβ is not essential for the chemotactic response of neutrophils, its role in lymphocyte migration has not been clearly defined. We compared the chemotactic response of peripheral T cells derived from wild-type mice with mice containing loss-of-function mutations in both of the two predominant lymphocyte PLCβ isoforms (PLCβ2 and PLCβ3), and demonstrate that loss of PLCβ2 and PLCβ3 significantly impaired T cell migration. Because second messengers generated by PLCβ lead to a rise in intracellular calcium and activation of PKC, we analyzed which of these responses was critical for the PLCβ-mediated chemotaxis. Intracellular calcium chelation decreased the chemotactic response of wild-type lymphocytes, but pharmacologic inhibition of several PKC isoforms had no effect. Furthermore, calcium efflux induced by stromal cell-derived factor-1α was undetectable in PLCβ2β3-null lymphocytes, suggesting that the migration defect is due to the impaired ability to increase intracellular calcium. This study demonstrates that, in contrast to neutrophils, phospholipid second messengers generated by PLCβ play a critical role in T lymphocyte chemotaxis.

Pleckstrin-2 is composed of 2 pleckstrin homology (PH) domains and a disheveled-Egl-10-pleckstrin (DEP) domain. A lipid-binding assay revealed that pleckstrin-2 binds with greatest affinity to D3 and D5 phosphoinositides. Pleckstrin-2 expressed in Jurkat T cells bound to the cellular membrane and enhanced actindependent spreading only after stimulation of the T-cell antigen receptor or the integrin ␣4␤1. A pleckstrin-2 variant containing point mutations in both PH domains failed to associate with the Jurkat membrane and had no effect on spreading under the same conditions. Although still membrane bound, a pleckstrin-2 variant containing point mutations in the DEP domain demonstrated a decreased ability to induce membrane ruffles and spread. Pleckstrin-2 also colocalized with actin at the immune synapse and integrin clusters via its PH domains. Although pleckstrin-2 can bind to purified D3 and D5 phosphoinositides, the intracellular membrane association of pleckstrin-2 and cell spreading are dependent on D3 phosphoinositides, because these effects were disrupted by pharmacologic inhibition of phosphatidylinositol 3-kinase (PI3K). Our results indicate that pleckstrin-2 uses its modular domains to bind to membraneassociated phosphatidylinositols generated by PI3K, whereby it coordinates with the actin cytoskeleton in lymphocyte spreading and immune synapse formation. IntroductionPleckstrin-1, the platelet and leukocyte C kinase substrate, is the major substrate of protein kinase C (PKC) in platelets, monocytes, macrophages, lymphocytes, and granulocytes. 1 Its 350 amino acid sequence can be divided into 3 motifs: PH domains at the aminoand carboxy-termini of the molecule and an intervening DEP domain. 2-4 A short stretch of amino acids between the aminoterminal PH domain and the DEP domain contains 3 sites of phosphorylation (Ser113, Thr114, and Ser117) by PKC, which are essential for the function of pleckstrin-1. [5][6][7] PH domains have been identified in approximately 252 other human proteins (Simple Modular Architecture Research Tool [SMART] database) and are found in many molecules involved in cellular signaling, cytoskeletal organization, membrane trafficking, and phospholipid modification. 8 Previous results have suggested that PH domains mediate binding of their host proteins to certain phosphoinositides. [9][10][11] Consistent with this hypothesis, nearly all PH domain-containing proteins require membrane association for their function in signal transduction, including pathways that contribute to cytoskeletal assembly, membrane budding, and fusion. 4,7,[12][13][14][15] DEP homology domains are present in numerous signaling proteins, 3,16 including 67 found in the human genome (SMART database). The DEP domain is a protein module of approximately 100 amino acids, first found in the signaling proteins disheveled, Egl-10, and pleckstrin. 3 The domain is also present in certain kinases, regulators of G-protein signaling proteins (RGSs), and Epac, the cyclic adenosine monophosphateregulated guanine nucleotide ...

Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided diagnostic (CAD) tool that has the potential to work both independent of and synergistically with expert analysis. Our tool operates on distributed metabolic changes across the whole brain measured by iPET to both diagnose and lateralize temporal lobe epilepsy (TLE). When diagnosing left TLE (LTLE) or right TLE (RTLE) vs. non-epileptic seizures (NES), our accuracy in reproducing the results of the gold standard long term video-EEG monitoring was 82% [95% confidence interval (CI) 69–90%] or 88% (95% CI 76–94%), respectively. The classifier that both diagnosed and lateralized the disease had overall accuracy of 76% (95% CI 66–84%), where 89% (95% CI 77–96%) of patients correctly identified with epilepsy were correctly lateralized. When identifying LTLE, our CAD tool utilized metabolic changes across the entire brain. By contrast, only temporal regions and the right frontal lobe cortex, were needed to identify RTLE accurately, a finding consistent with clinical observations and indicative of a potential pathophysiological difference between RTLE and LTLE. The goal of CADs is to complement – not replace – expert analysis. In our dataset, the accuracy of manual analysis (MA) of iPET (∼80%) was similar to CAD. The square correlation between our CAD tool and MA, however, was only 30%, indicating that our CAD tool does not recreate MA. The addition of clinical information to our CAD, however, did not substantively change performance. These results suggest that automated analysis might provide clinically valuable information to focus treatment more effectively.

Summary Objective Low-cost evidence-based tools are needed to facilitate the early identification of patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate diagnosis, patients with PNES do not receive interventions that address the cause of their seizures and therefore incur high medical costs and disability due to an uncontrolled seizure disorder. Both seizures and comorbidities may contribute to this high cost. Methods Based on data from 1,365 adult patients with video-electroencephalography confirmed diagnoses from a single center, we used logistic and Poisson regression to compare the total number of comorbidities, number of medications and presence of specific comorbidities in five mutually exclusive groups of diagnoses: epileptic seizures (ES) only, PNES only, mixed PNES and ES, physiologic nonepileptic seizure-like events, and inconclusive monitoring. To determine the diagnostic utility of comorbid diagnoses and medication history to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and age, trained using a retrospective database and validated using a prospective database. Results Our model differentiated PNES only from ES only with a prospective accuracy of 78% (95% CI 72–84%) and AUC of 79%. With a few exceptions, the number of comorbidities and medications was more predictive than a specific comorbidity. Comorbidities associated with PNES were asthma, chronic pain and migraines (p<0.01). Comorbidities associated with ES were diabetes mellitus and non-metastatic neoplasm (p<0.01). The population-level analysis suggested that patients with mixed PNES plus ES may be a distinct population from patients with either condition alone. Significance An accurate patient-reported past medical history and medication history can be useful when screening for possible PNES. Our prospectively validated and objective score may assist in the interpretation of the medication and medical history in the context of the seizure description and history.