BIOMARKERS AND NEUROBEHAVIORAL DIAGNOSIS

REVEALS: an open-source multi-camera GUI for rodent behavior acquisition

Deciphering the rich repertoire of mouse behavior is crucial for understanding the functions of both the healthy and diseased brain. However, the current landscape lacks effective, affordable, and accessible methods for acquiring such data, especially when employing multiple cameras simultaneously. We have developed REVEALS (Rodent Behavior Multi-Camera Laboratory Acquisition), a graphical user interface for acquiring rodent behavioral data via commonly used USB3 cameras. REVEALS allows for user-friendly control of recording from one or multiple cameras simultaneously while streamlining the data acquisition process, enabling researchers to collect and analyze large datasets efficiently. We release this software package as a stand-alone, open-source framework for researchers to use and modify according to their needs. We describe the details of the graphical user interface implementation, including the camera control software and the video recording functionality. We validate results demonstrating the graphical user interface's stability, reliability, and accuracy for capturing rodent behavior using DeepLabCut in various behavioral tasks. REVEALS can be incorporated into existing DeepLabCut, MoSeq, or other custom pipelines to analyze complex behavior. In summary, REVEALS offers an interface for collecting behavioral data from single or multiple perspectives, which, when combined with deep learning algorithms, enables the scientific community to identify and characterize complex behavioral phenotypes.

Etiologic Evaluation of Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is clinically and etiologically heterogeneous. A causal genetic variant can be identified in approximately 20% to 25% of affected individuals with current clinical genetic testing, and all patients with an ASD diagnosis should be offered genetic etiologic evaluation. We suggest that exome sequencing with copy number variant coverage should be the first-line etiologic evaluation for ASD. Neuroimaging, neurophysiologic, metabolic, and other biochemical evaluations can provide insight into the pathophysiology of ASD but should be recommended in the appropriate clinical circumstances.

REVEALS: An Open Source Multi Camera GUI For Rodent Behavior Acquisition

Understanding the rich behavioral data generated by mice is essential for deciphering the function of the healthy and diseased brain. However, the current landscape lacks effective, affordable, and accessible methods for acquiring such data, especially when employing multiple cameras simultaneously. We have developed REVEALS (Rodent BEhaVior Multi-camErA Laboratory AcquiSition), a graphical user interface (GUI) written in python for acquiring rodent behavioral data via commonly used USB3 cameras. REVEALS allows for user-friendly control of recording from one or multiple cameras simultaneously while streamlining the data acquisition process, enabling researchers to collect and analyze large datasets efficiently. We release this software package as a stand-alone, open-source framework for researchers to use and modify according to their needs. We describe the details of the GUI implementation, including the camera control software and the video recording functionality. We validate results demonstrating the GUI's stability, reliability, and accuracy for capturing and analyzing rodent behavior using DeepLabCut pose estimation in both an object and social interaction assay. REVEALS can also be incorporated into other custom pipelines to analyze complex behavior, such as MoSeq. In summary, REVEALS provides an interface for collecting behavioral data from one or multiple perspectives that, combined with deep learning algorithms, will allow the scientific community to discover and characterize complex behavioral phenotypes to understand brain function better.

Evaluating causal psychological models: A study of language theories of autism using a large sample

We used a large convenience sample (n = 22,223) from the Simons Powering Autism Research (SPARK) dataset to evaluate causal, explanatory theories of core autism symptoms. In particular, the data-items collected supported the testing of theories that posited altered language abilities as cause of social withdrawal, as well as alternative theories that competed with these language theories. Our results using this large dataset converge with the evolution of the field in the decades since these theories were first proposed, namely supporting primary social withdrawal (in some cases of autism) as a cause of altered language development, rather than vice versa. To accomplish the above empiric goals, we used a highly theory-constrained approach, one which differs from current data-driven modeling trends but is coherent with a very recent resurgence in theory-driven psychology. In addition to careful explication and formalization of theoretical accounts, we propose three principles for future work of this type: specification, quantification, and integration. Specification refers to constraining models with pre-existing data, from both outside and within autism research, with more elaborate models and more veridical measures, and with longitudinal data collection. Quantification refers to using continuous measures of both psychological causes and effects, as well as weighted graphs. This approach avoids "universality and uniqueness" tests that hold that a single cognitive difference could be responsible for a heterogeneous and complex behavioral phenotype. Integration of multiple explanatory paths within a single model helps the field examine for multiple contributors to a single behavioral feature or to multiple behavioral features. It also allows integration of explanatory theories across multiple current-day diagnoses and as well as typical development.

RNA biomarkers for alcohol use disorder

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

Independent evaluation of the harvard automated processing pipeline for Electroencephalography 1.0 using multi-site EEG data from children with Fragile X Syndrome

BACKGROUND

The Harvard Automatic Processing Pipeline for Electroencephalography (HAPPE) is a computerized EEG data processing pipeline designed for multiple site analysis of populations with neurodevelopmental disorders. This pipeline has been validated in-house by the developers but external testing using real-world datasets remains to be done.

NEW METHOD

Resting and auditory event-related EEG data from 29 children ages 3-6 years with Fragile X Syndrome as well as simulated EEG data was used to evaluate HAPPE's noise reduction techniques, data standardization features, and data integration compared to traditional manualized processing.

RESULTS

For the real EEG data, HAPPE pipeline showed greater trials retained, greater variance retained through independent component analysis (ICA) component removal, and smaller kurtosis than the manual pipeline; the manual pipeline had a significantly larger signal-to-noise ratio (SNR). For simulated EEG data, correlation between the pure signal and processed data was significantly higher for manually-processed data compared to HAPPE-processed data. Hierarchical linear modeling showed greater signal recovery in the manual pipeline with the exception of the gamma band signal which showed mixed results.

COMPARISON WITH EXISTING METHODS

SNR and simulated signal retention was significantly greater in the manually-processed data than the HAPPE-processed data. Signal reduction may negatively affect outcome measures.

CONCLUSIONS

The HAPPE pipeline benefits from less active processing time and artifact reduction without removing segments. However, HAPPE may bias toward elimination of noise at the cost of signal. Recommended implementation of the HAPPE pipeline for neurodevelopmental populations depends on the goals and priorities of the research.

Neurobehavioral Biomarkers: An EEG Family Reunion

SUMMARY

The field of clinical EEG has had an uneasy relationship with the use of this technology for clinical cognitive applications and often for good reason. However, apart from its clinical use, EEG has had a tradition as a major tool in cognitive psychology and cognitive neuroscience dating back at least to the 1960s. Based on accumulated knowledge from its research application, EEG-based biomarkers are beginning to see applications in clinical trials and may eventually enter clinical care. We address concerns surrounding quality control, the treatment of artifact, and normal variants and how developments in engineering, biomarker validation, and implementation science rigorously applied to these tools can lead to well-justified approaches.

Dissociation in Neural Correlates of Hyperactive/Impulsive vs. Inattentive Symptoms in Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders characterized in current diagnostic criteria by two dominant symptoms, inattention and hyperactivity/impulsivity. Here, we show that task-related alpha (8-12 Hz) interhemispheric connectivity changes, as assessed during a unimanual finger-tapping task, is correlated with inattentive symptom severity (r = 0.55, p = 0.01) but not with severity of hyperactive/impulsive symptoms. Prior published analyses of the same dataset have already show that alpha event-related desynchronization (ERD) in the hemisphere contralateral to unimanual tapping is related to hyperactive/impulsive symptom severity (r = 0.43, p = 0.04) but not to inattentive symptom severity. Our findings demonstrate a neurobiological dissociation in ADHD symptom severity, with implications for understanding the structure of endophenotypes in the disorder as well as for biomarker development.

Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies

Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.

Associations between Task-Related Modulation of Motor-Evoked Potentials and EEG Event-Related Desynchronization in Children with ADHD

Children with attention-deficit/hyperactivity disorder (ADHD) have previously shown a decreased magnitude of event-related desynchronization (ERD) during a finger-tapping task, with a large between-group effect. Because the neurobiology underlying several transcranial magnetic stimulation (TMS) measures have been studied in multiple contexts, we compared ERD and 3 TMS measures (resting motor threshold [RMT], short-interval cortical inhibition [SICI], and task-related up-modulation [TRUM]) within 14 participants with ADHD (ages 8-12 years) and 17 control children. The typically developing (TD) group showed a correlation between greater RMT and greater magnitude of alpha (10-13 Hz, here) ERD, and there was no diagnostic interaction effect, consistent with a rudimentary model of greater needed energy input to stimulate movement. Similarly, inhibition measured by SICI was also greater in the TD group when the magnitude of movement-related ERD was higher; there was a miniscule diagnostic interaction effect. Finally, TRUM during a response-inhibition task showed an unanticipated pattern: in TD children, the greater TMS task modulation (TRUM) was associated with a smaller magnitude of ERD during finger-tapping. The ADHD group showed the opposite direction of association: Greater TRUM was associated with larger magnitude of ERD. Prior EEG results have demonstrated specific alterations of task-related modulation of cortical physiology, and the current results provide a fulcrum for multimodal study.