Invasive Computational Psychiatry

Sáez, Ignacio; Gu, Xiaosi

doi:10.1016/j.biopsych.2022.09.032

Cited by 9 publications

(6 citation statements)

References 140 publications

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“…12 Most recently, computational models based on mathematical modeling for understanding behaviors and psychiatric disorders have emerged. [13][14][15] For example, the theory of constructed emotion is based on concepts from predictive processing and offers a framework for reconceptualizing functional neurological disorders. 16 Although scientifically rigorous, these emerging computational models aim to explain psychiatric phenomena rather than provide a new classification system of disorders.…”

Section: Existing Models Of Psychiatric Disorders: Conceptualization ...mentioning

confidence: 99%

Proposal for a Mechanistic Disease Conceptualization in Clinical Neurosciences: The Neural Network Components (NNC) Model

Nassan

2024

Harv Rev Psychiatry

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Clinical neurosciences, and psychiatry specifically, have been challenged by the lack of a comprehensive and practical framework that explains the core mechanistic processes of variable psychiatric presentations. Current conceptualization and classification of psychiatric presentations are primarily centered on a non-biologically based clinical descriptive approach. Despite various attempts, advances in neuroscience research have not led to an improved conceptualization or mechanistic classification of psychiatric disorders. This perspective article proposes a new—work-in-progress—framework for conceptualizing psychiatric presentations based on neural network components (NNC). This framework could guide the development of mechanistic disease classification, improve understanding of underpinning pathology, and provide specific intervention targets. This model also has the potential to dissolve artificial barriers between the fields of psychiatry and neurology.

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Section: Existing Models Of Psychiatric Disorders: Conceptualization ...mentioning

confidence: 99%

Proposal for a Mechanistic Disease Conceptualization in Clinical Neurosciences: The Neural Network Components (NNC) Model

Nassan

2024

Harv Rev Psychiatry

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“…Importantly, this would also allow investigation of putative causal links between neurobiological alterations and affective disorders. Opportunities for such research are obviously very limited or not possible in human participants (Saez & Gu, 2023 ), so non-human subjects offer far richer opportunities to advance our understanding of affect. More specifically, the influence of affect could be directly parameterized in the model, with clear hypotheses about the potential neural substrates that might be associated with these parameters.…”

Section: Computational Approaches To Study Affective Disorders Using ...mentioning

confidence: 99%

A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals

Neville,

Mendl,

Paul

et al. 2023

Cogn Affect Behav Neurosci

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Objective measures of animal emotion-like and mood-like states are essential for preclinical studies of affective disorders and for assessing the welfare of laboratory and other animals. However, the development and validation of measures of these affective states poses a challenge partly because the relationships between affect and its behavioural, physiological and cognitive signatures are complex. Here, we suggest that the crisp characterisations offered by computational modelling of the underlying, but unobservable, processes that mediate these signatures should provide better insights. Although this computational psychiatry approach has been widely used in human research in both health and disease, translational computational psychiatry studies remain few and far between. We explain how building computational models with data from animal studies could play a pivotal role in furthering our understanding of the aetiology of affective disorders, associated affective states and the likely underlying cognitive processes involved. We end by outlining the basic steps involved in a simple computational analysis.

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“…Intraoperative electrocorticography (ioECoG) offers neurosurgeons instantaneous readouts of pathologic neuronal activity. 6 It may help to delineate the epileptogenic brain region, especially in focal and superficial lesions, map out functional areas, and identify residual epileptiform abnormalities after the initial surgical process. 7 , 8 …”

Section: Introductionmentioning

confidence: 99%

Seizure Outcome After Intraoperative Electrocorticography-Tailored Epilepsy Surgery

Guo,

Wang,

van 't Klooster

et al. 2024

Neurology

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Background and Objectives Tailoring epilepsy surgery using intraoperative electrocorticography (ioECoG) has been debated, and modest number of epilepsy surgery centers apply this diagnostic method. We assessed the current evidence to use ioECoG-tailored epilepsy surgery for improving postsurgical outcome. Methods PubMed and Embase were searched for original studies reporting on ≥10 cases who underwent ioECoG-tailored surgery for epilepsy, with a follow-up of at least 6 months. We used a random-effects model to calculate the overall rate of patients achieving favorable seizure outcome (FSO), defined as Engel class I, ILAE class 1, or seizure-free status. Meta-regression was used to investigate potential sources of heterogeneity. We calculated the odds ratio (OR) for estimating variables on FSO:ioECoG vs non-ioECoG-tailored surgery (if included studies contained patients with non-ioECoG-tailored surgery), ioECoG-tailored epilepsy surgery in children vs adults, temporal (TL) vs extratemporal lobe (eTL), MRI-positive vs MRI-negative, and complete vs incomplete resection of tissue that generated interictal epileptiform discharges (IEDs). A Bayesian network meta-analysis was conducted for underlying pathologies. We assessed the evidence certainty using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). Results Eighty-three studies (82 observational studies, 1 trial) comprising 3,631 patients with ioECoG-tailored surgery were included. The overall pooled rate of patients who attained FSO after ioECoG-tailored surgery was 74% (95% CI 71–77) with significant heterogeneity, which was predominantly attributed to pathologies and seizure outcome classifications. Twenty-two studies contained non–ioECoG-tailored surgeries. IoECoG-tailored surgeries reached a higher rate of FSO than non–ioECoG-tailored surgeries (OR 2.10 [95% CI 1.37–3.24]; p < 0.01; very low certainty). Complete resection of tissue that displayed IEDs in ioECoG predicted FSO better compared with incomplete resection (OR 3.04 [1.76–5.25]; p < 0.01; low certainty). We found insignificant difference in FSO after ioECoG-tailored surgery in children vs adults, TL vs eTL, or MRI-positive vs MRI-negative. The network meta-analysis showed that the odds of FSO was lower for malformations of cortical development than for tumors (OR 0.47 95% credible interval 0.25–0.87). Discussion Although limited by low-quality evidence, our meta-analysis shows a relatively good surgical outcome (74% FSO) after epilepsy surgery with ioECoG, especially in tumors, with better outcome for ioECoG-tailored surgeries in studies describing both and better outcome after complete removal of IED areas.

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Invasive Computational Psychiatry

Cited by 9 publications

References 140 publications

Proposal for a Mechanistic Disease Conceptualization in Clinical Neurosciences: The Neural Network Components (NNC) Model

Proposal for a Mechanistic Disease Conceptualization in Clinical Neurosciences: The Neural Network Components (NNC) Model

A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals

Seizure Outcome After Intraoperative Electrocorticography-Tailored Epilepsy Surgery

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