Female sex and burden of depressive symptoms predict insufficient response to telemedical treatment in adult attention-deficit/hyperactivity disorder: results from a naturalistic patient cohort during the COVID-19 pandemic

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is a chronic neuropsychiatric disorder, that typically manifests itself during childhood and persists in a majority of the affected individuals into adulthood, negatively affecting physical and mental health. Previous studies have shown detrimental effects of the COVID-19 pandemic on mental health in individuals with ADHD. Thus, telemedicine could be a useful tool for optimizing treatment-outcomes in adult ADHD by improving treatment adherence and persistence. However, data on telemedical treatment outcomes in adult patients with ADHD is scarce.MethodsWe report here the sub-cohort analysis of a naturalistic cohort of adult patients (N = 254) recruited between April 2020–April 2021, comparing the effects of telemedical treatment on participants either clinically diagnosed with depression (N = 54) or ADHD (N = 67). Participants were asked to fill out the WHO-5 repetitively during >12 weeks of telemedical treatment. Furthermore scores of WHO-5, SCL-90R and BDI-II, psychopathology, psychosocial functioning, sociodemographic data, medical records and a feedback survey were analyzed for both groups and compared. Participants with ADHD were further stratified according to the development of well-being during the study period in order to identify factors associated with a satisfactory treatment outcome.ResultsParticipants with depression reported a significant improvement of well-being during the course of the study, while no such effect could be seen in participants with ADHD on a group level. Despite the good outcome, participants with depression were more severely affected at baseline, with significantly worse psychopathology and a more precarious labor and financial situation. A detailed analysis of ADHD participants without clinical improvement revealed significantly higher BDI-II scores than for ADHD participants with a satisfactory outcome (p = 0.03, Mann–Whitney-U-Test), suggesting successful treatment was hampered by the combination of ADHD and depressive symptoms. Furthermore, female sex among ADHD patients was correlated with an unfavorable treatment outcome during the course of the study (p = 0.001, Spearman correlation) as well as living with children (p = 0.02, Spearman correlation).ConclusionBesides screening for depressive symptoms before telemedical treatment, future research should address the specific needs of female ADHD patients as these patients may be at a particularly high risk of being overburdened with family work

EXPLORE: a novel deep learning-based analysis method for exploration behaviour in object recognition tests

Object recognition tests are widely used in neuroscience to assess memory function in rodents. Despite the experimental simplicity of the task, the interpretation of behavioural features that are counted as object exploration can be complicated. Thus, object exploration is often analysed by manual scoring, which is time-consuming and variable across researchers. Current software using tracking points often lacks precision in capturing complex ethological behaviour. Switching or losing tracking points can bias outcome measures. To overcome these limitations we developed “EXPLORE”, a simple, ready-to use and open source pipeline. EXPLORE consists of a convolutional neural network trained in a supervised manner, that extracts features from images and classifies behaviour of rodents near a presented object. EXPLORE achieves human-level accuracy in identifying and scoring exploration behaviour and outperforms commercial software with higher precision, higher versatility and lower time investment, in particular in complex situations. By labeling the respective training data set, users decide by themselves, which types of animal interactions on objects are in- or excluded, ensuring a precise analysis of exploration behaviour. A set of graphical user interfaces (GUIs) provides a beginning-to-end analysis of object recognition tests, accelerating a fast and reproducible data analysis without the need of expertise in programming or deep learning

Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning

Motor behaviour analysis is essential to biomedical research and clinical diagnostics as it provides a non-invasive strategy for identifying motor impairment and its change caused by interventions. State-of-the-art instrumented movement analysis is time- and cost-intensive, since it requires placing physical or virtual markers. Besides the effort required for marking keypoints or annotations necessary for training or finetuning a detector, users need to know the interesting behaviour beforehand to provide meaningful keypoints. We introduce unsupervised behaviour analysis and magnification (uBAM), an automatic deep learning algorithm for analysing behaviour by discovering and magnifying deviations. A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement. Besides discovering and quantifying deviations in behaviour, we also propose a generative model for visually magnifying subtle behaviour differences directly in a video without requiring a detour via keypoints or annotations. Essential for this magnification of deviations even across different individuals is a disentangling of appearance and behaviour. Evaluations on rodents and human patients with neurological diseases demonstrate the wide applicability of our approach. Moreover, combining optogenetic stimulation with our unsupervised behaviour analysis shows its suitability as a non-invasive diagnostic tool correlating function to brain plasticity.Comment: Published in Nature Machine Intelligence (2021), https://rdcu.be/ch6p

Intranasal delivery of full-length anti-Nogo-A antibody: A potential alternative route for therapeutic antibodies to central nervous system targets

Antibody delivery to the CNS remains a huge hurdle for the clinical application of antibodies targeting a CNS antigen. The blood-brain barrier and blood-CSF barrier restrict access of therapeutic antibodies to their CNS targets in a major way. The very high amounts of therapeutic antibodies that are administered systemically in recent clinical trials to reach CNS targets are barely viable cost-wise for broad, routine applications. Though global CNS delivery of antibodies can be achieved by intrathecal application, these procedures are invasive. A non-invasive method to bring antibodies into the CNS reliably and reproducibly remains an important unmet need in neurology. In the present study, we show that intranasal application of a mouse monoclonal antibody against the neurite growth-inhibiting and plasticity-restricting membrane protein Nogo-A leads to a rapid transfer of significant amounts of antibody to the brain and spinal cord in intact adult rats. Daily intranasal application for 2 wk of anti-Nogo-A antibody enhanced growth and compensatory sprouting of corticofugal projections and functional recovery in rats after large unilateral cortical strokes. These findings are a starting point for clinical translation for a less invasive route of application of therapeutic antibodies to CNS targets for many neurological indications

State-of-the-Art Techniques to Causally Link Neural Plasticity to Functional Recovery in Experimental Stroke Research

Current experimental stroke research faces the same challenge as neuroscience: to transform correlative findings in causative ones. Research of recent years has shown the tremendous potential of the central nervous system to react to noxious stimuli such as a stroke: Increased plastic changes leading to reorganization in form of neuronal rewiring, neurogenesis, and synaptogenesis, accompanied by transcriptional and translational turnover in the affected cells, have been described both clinically and in experimental stroke research. However, only minor attempts have been made to connect distinct plastic remodeling processes as causative features for specific behavioral phenotypes. Here, we review current state-of the art techniques for the examination of cortical reorganization and for the manipulation of neuronal circuits as well as techniques which combine anatomical changes with molecular profiling. We provide the principles of the techniques together with studies in experimental stroke research which have already applied the described methodology. The tools discussed are useful to close the loop from our understanding of stroke pathology to the behavioral outcome and may allow discovering new targets for therapeutic approaches. The here presented methods open up new possibilities to assess the efficiency of rehabilitative strategies by understanding their external influence for intrinsic repair mechanisms on a neurobiological basis.ISSN:2090-5904ISSN:1687-544

Finding an optimal rehabilitation paradigm after stroke: enhancing fiber growth and training of the brain at the right moment

After stroke the central nervous system reveals a spectrum of intrinsic capacities to react as a highly dynamic system which can change the properties of its circuits, form new contacts, erase others, and remap related cortical and spinal cord regions. This plasticity can lead to a surprising degree of spontaneous recovery. It includes the activation of neuronal molecular mechanisms of growth and of extrinsic growth promoting factors and guidance signals in the tissue. Rehabilitative training and pharmacological interventions may modify and boost these neuronal processes, but almost nothing is known on the optimal timing of the different processes and therapeutic interventions and on their detailed interactions. Finding optimal rehabilitation paradigms requires an optimal orchestration of the internal processes of re‐organization and the therapeutic interventions in accordance with defined plastic time windows.In this review we summarize the mechanisms of spontaneous plasticity after stroke and experimental interventions to enhance growth and plasticity, with an emphasis on anti‐Nogo‐A immunotherapy. We highlight critical time windows of growth and of rehabilitative training and consider different approaches of combinatorial rehabilitative schedules. Finally, we discuss potential future strategies for designing repair and rehabilitation paradigms by introducing a 3 step model: determination of the metabolic and plastic status of the brain, pharmacological enhancement of its plastic mechanisms, and stabilization of newly formed functional connections by rehabilitative training

Parsing Science - Stroke Recovery with Light

<a href="https://www.researchgate.net/profile/Anna_Sophia_Wahl" rel="noopener" target="_blank">Anna-Sophia Wahl</a> is a neuroscientist with the <a href="https://www.hifo.uzh.ch/en/research/schwab/schwabPeople.html" rel="noopener" target="_blank">Brain Research Institute</a> at the Swiss Federal Institute of Technology, in Zurich, as well as a physician with the Central Institute of Mental Health in Mannheim, Germany. She spoke with us about her article "<a href="https://www.nature.com/articles/s41467-017-01090-6" rel="noopener" target="_blank">Optogenetically stimulating intact rat corticospinal tract post-stroke restores motor control through regionalized functional circuit formation</a>" (<a href="https://www.nature.com/articles/s41467-017-01090-6.pdf" rel="noopener" target="_blank">PDF</a>) published on October 30, 2017 in the journal <a href="https://www.nature.com/ncomms/" rel="noopener" target="_blank"><i>Nature Communications</i></a>. The article was published with multiple co-authors

EXPLORE: a novel deep learning-based analysis method for exploration behaviour in object recognition tests

Object recognition tests are widely used in neuroscience to assess memory function in rodents. Despite the experimental simplicity of the task, the interpretation of behavioural features that are counted as object exploration can be complicated. Thus, object exploration is often analysed by manual scoring, which is time-consuming and variable across researchers. Current software using tracking points often lacks precision in capturing complex ethological behaviour. Switching or losing tracking points can bias outcome measures. To overcome these limitations we developed "EXPLORE", a simple, ready-to use and open source pipeline. EXPLORE consists of a convolutional neural network trained in a supervised manner, that extracts features from images and classifies behaviour of rodents near a presented object. EXPLORE achieves human-level accuracy in identifying and scoring exploration behaviour and outperforms commercial software with higher precision, higher versatility and lower time investment, in particular in complex situations. By labeling the respective training data set, users decide by themselves, which types of animal interactions on objects are in- or excluded, ensuring a precise analysis of exploration behaviour. A set of graphical user interfaces (GUIs) provides a beginning-to-end analysis of object recognition tests, accelerating a fast and reproducible data analysis without the need of expertise in programming or deep learning.ISSN:2045-232