Ariana E. Anderson

Mindfulness, emotional dysregulation, impulsivity, and stress proneness among hypersexual patients

OBJECTIVE The current study explores relationships between mindfulness, emotional regulation, impulsivity, and stress proneness in a sample of participants recruited in a Diagnostic and Statistical Manual of Mental Disorder Fifth Edition Field Trial for Hypersexual Disorder and healthy controls to assess whether mindfulness attenuates symptoms of hypersexuality. METHOD Hierarchal regression analysis was used to assess whether significant relationships between mindfulness and hypersexuality exist beyond associations commonly found with emotional dysregulation, impulsivity, and stress proneness in a sample of male hypersexual patients (n = 40) and control subjects (n = 30). RESULTS Our results show a robust inverse relationship of mindfulness to hypersexuality over and above associations with emotional regulation, impulsivity, and stress proneness. CONCLUSIONS These results suggest that mindfulness may be a meaningful component of successful therapy among patients seeking help for hypersexual behavior in attenuating hypersexuality, improving affect regulation, stress coping, and increasing tolerance for desires to act on maladaptive sexual urges and impulses.

Classification of spatially unaligned fMRI scans.

The analysis of fMRI data is challenging because they consist generally of a relatively modest signal contained in a high-dimensional space: a single scan can contain millions of voxel recordings over space and time. We present a method for classification and discrimination among fMRI that is based on modeling the scans as distance matrices, where each matrix measures the divergence of spatial network signals that fluctuate over time. We used single-subject independent components analysis (ICA), decomposing an fMRI scan into a set of statistically independent spatial networks, to extract spatial networks and time courses from each subject that have unique relationship with the other components within that subject. Mathematical properties of these relationships reveal information about the infrastructure of the brain by measuring the interaction between and strength of the components. Our technique is unique, in that it does not require spatial alignment of the scans across subjects. Instead, the classifications are made solely on the temporal activity taken by the subjects unique ICs. Multiple scans are not required and multivariate classification is implementable, and the algorithm is effectively blind to the subject-uniform underlying task paradigm. Classification accuracy of up to 90% was realized on a resting-scanned schizophrenia/normal dataset and a tasked multivariate Alzheimers/old/young dataset. We propose that the ICs represent a plausible set of imaging basis functions consistent with network-driven theories of neural activity in which the observed signal is an aggregate of independent spatial networks having possibly dependent temporal activity.

Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET.

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.

Automated diagnosis of epilepsy using EEG power spectrum

Interictal electroencephalography (EEG) has clinically meaningful limitations in its sensitivity and specificity in the diagnosis of epilepsy because of its dependence on the occurrence of epileptiform discharges. We have developed a computer‐aided diagnostic (CAD) tool that operates on the absolute spectral energy of the routine EEG and has both substantially higher sensitivity and negative predictive value than the identification of interictal epileptiform discharges. Our approach used a multilayer perceptron to classify 156 patients admitted for video‐EEG monitoring. The patient population was diagnostically diverse; 87 were diagnosed with either generalized or focal seizures. The remainder of the patients were diagnosed with nonepileptic seizures. The sensitivity was 92% (95% confidence interval [CI] 85–97%) and the negative predictive value was 82% (95% CI 67–92%). We discuss how these findings suggest that this CAD can be used to supplement event‐based analysis by trained epileptologists.

Social network analysis of children with autism spectrum disorder: Predictors of fragmentation and connectivity in elementary school classrooms

Although children with autism spectrum disorder are frequently included in mainstream classrooms, it is not known how their social networks change compared to typically developing children and whether the factors predictive of this change may be unique. This study identified and compared predictors of social connectivity of children with and without autism spectrum disorder using a social network analysis. Participants included 182 children with autism spectrum disorder and 152 children without autism spectrum disorder, aged 5–12 years in 152 general education K-5 classrooms. General linear models were used to compare how age, classroom size, gender, baseline connectivity, diagnosis, and intelligence quotient predicted changes in social connectivity (closeness). Gender and classroom size had a unique interaction in predicting final social connectivity and the change in connectivity for children with autism spectrum disorder; boys who were placed in larger classrooms showed increased social network fragmentation. This increased fragmentation for boys when placed in larger classrooms was not seen in typically developing boys. These results have implications regarding placement, intervention objectives, and ongoing school support that aimed to increase the social success of children with autism spectrum disorder in public schools.

Benzodiazepine sensitivity testing in the management of intractable seizure disorders in childhood

The use of benzodiazepine sensitivity testing in the management of 40 children with intractable seizure disorders was studied. The aetiology and clinical syndromes varied widely with myoclonic, atonic and complex absence seizures predominating. Twenty-five cases had mixed seizure disorders. There was, likewise, a wide range of EEG abnormalities. Seven cases were in non-convulsive status at the time of testing. Diazepam (0.2 mg/kg) was given slowly intravenously and its effect on the EEG was observed. In 21 cases epileptiform activity was abolished. No change was seen in 13 cases and an unusual result was seen in 3. There was a paradoxical response in 3 cases, two of these associated with clinical seizures. Only 1 child in non-convulsive status had a positive result. Following testing, 32 patients went on to long-term oral benzodiazepine treatment. Twenty-one of these patients showed subsequent clinical improvement and 16/21 (76%) had had a positive sensitivity test previously. Eleven of these patients did not improve on long-term treatment. Seven out of the 11 (64%) had had a negative sensitivity test. These results suggest that the benzodiazepine sensitivity test is of value in the long-term management of intractable seizure disorders in childhood, but also emphasise the variability and unpredictability of response to benzodiazepine treatment.

Diffusion Tensor Imaging of TBI: Potentials and Challenges.

Abstract Neuroimaging plays a critical role in the setting in traumatic brain injury (TBI). Diffusion tensor imaging (DTI) is an advanced magnetic resonance imaging technique that is capable of providing rich information on the brains neuroanatomic connectome. The purpose of this article is to systematically review the role of DTI and advanced diffusion techniques in the setting of TBI, including diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging, diffusion spectrum imaging, and q-ball imaging. We discuss clinical applications of DTI and review the DTI literature as it pertains to TBI. Despite the continued advancements in DTI and related diffusion techniques over the past 20 years, DTI techniques are sensitive for TBI at the group level only and there is insufficient evidence that DTI plays a role at the individual level. We conclude by discussing future directions in DTI research in TBI including the role of machine learning in the pattern classification of TBI.

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms

BACKGROUND Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. NEW METHOD The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. RESULTS AND COMPARISON WITH EXISTING METHOD The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p<0.001). Lower classification accuracy occurred when the extracted spatial maps contained more CSF regions (p<0.001). CONCLUSION The success of sparse coding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations.

Large sample group independent component analysis of functional magnetic resonance imaging using anatomical atlas-based reduction and bootstrapped clustering

Independent component analysis (ICA) is a popular method for the analysis of functional magnetic resonance imaging (fMRI) signals that is capable of revealing connected brain systems of functional significance. To be computationally tractable, estimating the independent components (ICs) inevitably requires one or more dimension reduction steps. Whereas most algorithms perform such reductions in the time domain, the input data are much more extensive in the spatial domain, and there is broad consensus that the brain obeys rules of localization of function into regions that are smaller in number than the number of voxels in a brain image. These functional units apparently reorganize dynamically into networks under different task conditions. Here we develop a new approach to ICA, producing group results by bagging and clustering over hundreds of pooled single‐subject ICA results that have been projected to a lower‐dimensional subspace. Averages of anatomically based regions are used to compress the single subject‐ICA results prior to clustering and resampling via bagging. The computational advantages of this approach make it possible to perform group‐level analyses on datasets consisting of hundreds of scan sessions by combining the results of within‐subject analysis, while retaining the theoretical advantage of mimicking what is known of the functional organization of the brain. The result is a compact set of spatial activity patterns that are common and stable across scan sessions and across individuals. Such representations may be used in the context of statistical pattern recognition supporting real‐time state classification.

Single trial decoding of belief decision making from EEG and fMRI data using independent components features

The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subjects decision response to a given propositional statement based on independent component (IC) features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM) results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities.