Search Results (127)

Creative insight is a core phenomenon of human cognition, often characterized by the sudden emergence of novel and contextually appropriate ideas. Classical models based on symbolic search or associative networks struggle to capture the non-linear, context-sensitive, and interference-driven aspects of insight. In this work, we propose a computational model of insight generation grounded in continuous-time quantum walks over weighted semantic graphs, where nodes represent conceptual units and edges encode associative relationships. By exploiting the principles of quantum superposition and interference, the model enables the probabilistic amplification of semantically distant but contextually relevant concepts, providing a plausible account of non-local transitions in thought. The model is implemented using standard Python 3.10 libraries and is available both as an interactive fully reproducible Google Colab notebook and a public repository with code and derived datasets. Comparative experiments on ConceptNet-derived subgraphs, including the Candle Problem, 20 Remote Associates Test triads, and Alternative Uses, show that, relative to classical diffusion, quantum walks concentrate more probability on correct targets (higher AUC and peaks reached earlier) and, in open-ended settings, explore more broadly and deeply (higher entropy and coverage, larger expected radius, and faster access to distant regions). These findings are robust under normalized generators and a common time normalization, align with our formal conditions for transient interference-driven amplification, and support quantum-like dynamics as a principled process model for key features of insight. Full article

Nuclear receptor subfamily 4 group A member 2 (NR4A2) is a transcription factor that regulates the expression of different genes involved in essential biological processes, including cell proliferation, neuronal development, immune response, cellular stress, apoptosis, DNA repair, and angiogenesis. The gene encoding this transcription factor is called NR4A2 and has been identified as an immediate early gene. Moreover, research in animal models and clinical trials has suggested an association between reduced NR4A2 gene expression and some neurodegenerative diseases and psychiatric disorders. These include Parkinson’s disease, Alzheimer’s disease progression, schizophrenia, substance abuse (alcohol and amphetamines), neurodevelopmental disorders, and cognitive imairment. NR4A2 activity is controlled at multiple levels, including transcriptional and post-transcriptional regulation of its gene expression, such as translational and post-translational processes. This review summarizes the current knowledge of the NR4A2 gene, encompassing its structure and the molecular mechanisms that regulate its expression. The key epigenetic mechanisms that regulate its gene expression are emphasized, including DNA methylation, histone deacetylation, and regulation by microRNAs. It also addresses its role in central nervous system pathologies associated with dysregulation of NR4A2 gene expression. Finally, we discuss the potential of these regulatory mechanisms as biomarkers and therapeutic targets for neurodegenerative diseases and psychiatric disorders. Full article

Schizophrenia is a highly polygenic disease, and several genetic variants associated with the disease converge on altered synaptic homeostasis. In particular, the gene encoding complement component 4 (C4) showed the strongest association with schizophrenia, and this protein is involved in complement-dependent and microglia-mediated synaptic pruning. As a matter of fact, microglia are overactive in schizophrenia, and reduced synaptic arborization, especially in the prefrontal cortex (PFC), is an established hallmark of schizophrenia, likely associated with gray matter loss, cortical thinning, hypofrontality, and deficit syndrome. The recent development of a new radioligand targeting the synaptic vesicle glycoprotein 2A (SV2A) demonstrated in vivo lower synaptic density at the PFC level in individuals with schizophrenia, corroborating the synaptic hypothesis of thedisease first proposed by Feinberg in 1982. Interestingly, robust preclinical evidence (in vitro and animal models) showed the ability of psychedelics to promote neuroplasticity and synaptogenesis, potentially counteracting the excessive synaptic loss, restoring volume loss, and possibly explaining improvements in negative and cognitive symptoms described by old clinical studies. Overall, microdoses should be explored first as a possible treatment in a selected sample of patients affected by deficit schizophrenia, followed by low and full doses if encouraging results were to emerge. Full article

Background: The human visual system is capable of processing large quantities of visual objects with varying levels of abstraction. The brain also exhibits hierarchical integration and learning capabilities that combine various attributes of visual objects (e.g., color, shape, local features, and categories) into coherent representations. However, prevailing theories in visual neuroscience employ simple stimuli or natural images with uncontrolled feature correlations, which constrains the systematic investigation of multidimensional representation dynamics. Methods: In this study, we aimed to bridge this methodological gap by developing a novel large tangram paradigm in visual cognition research and proposing cognitive-associative encoding as a mathematical basis. Critical representation dimensions—including animacy, abstraction level, and local feature density—were computed across a public dataset of over 900 tangrams, enabling the construction of a hierarchical model of visual representation. Results: Neural responses to 85 representative images were recorded using Electroencephalography (n = 24), and subsequent behavioral analyses and neural decoding revealed that distinct representational dimensions are independently encoded and dynamically expressed at different stages of cognitive processing. Furthermore, representational similarity analysis and temporal generalization analysis indicated that higher-order cognitive processes, such as “change of mind,” reflect the selective activation or suppression of local feature processing. Conclusions: These findings demonstrate that tangram stimuli, structured through cognitive-associative encoding, provide a generalizable computational framework for investigating the dynamic stages of human visual object cognition. Full article

To address the semantic mismatch between limited textual descriptions in image captioning training datasets and the multi-semantic nature of images, as well as the underutilized external commonsense knowledge, this article proposes a novel image captioning model based on multi-step cross-attention cross-modal alignment and external commonsense knowledge enhancement. The model employs a backbone architecture comprising CLIP’s ViT visual encoder, Faster R-CNN, BERT text encoder, and GPT-2 text decoder. It incorporates two core mechanisms: a multi-step cross-attention mechanism that iteratively aligns image and text features across multiple rounds, progressively enhancing inter-modal semantic consistency for more accurate cross-modal representation fusion. Moreover, the model employs Faster R-CNN to extract region-based object features. These features are mapped to corresponding entities within the dataset through entity probability calculation and entity linking. External commonsense knowledge associated with these entities is then retrieved from the ConceptNet knowledge graph, followed by knowledge embedding via TransE and multi-hop reasoning. Finally, the fused multimodal features are fed into the GPT-2 decoder to steer caption generation, enhancing the lexical richness, factual accuracy, and cognitive plausibility of the generated descriptions. In the experiments, the model achieves CIDEr scores of 142.6 on MSCOCO and 78.4 on Flickr30k. Ablations confirm both modules enhance caption quality. Full article

Alzheimer’s Disease and Dementia (ADD) progresses along a continuum of cognitive decline, typically from Subjective Cognitive Impairment (SCI) to Mild Cognitive Impairment (MCI) and eventually to dementia. While many studies have focused on classifying these clinical stages, fewer have examined whether brain connectomes encode this continuum in a low-dimensional, interpretable form. Motivated by the hypothesis that structural brain connectomes undergo complex yet compact changes across cognitive decline, we propose a Graph Neural Network (GNN)-based framework that embeds these connectomes into a two-dimensional manifold to capture the evolving patterns of structural connectivity associated with cognitive deterioration. Using attention-based graph aggregation and Principal Component Analysis (PCA), we find that MCI subjects consistently occupy an intermediate position between SCI and ADD, and that the observed transitions align with known clinical biomarkers of ADD pathology. This hypothesis-driven analysis is further supported by the model’s robust separation performance, with ROC-AUC scores of 0.93 for ADD vs. SCI and 0.81 for ADD vs. MCI. These findings offer an interpretable and neurologically grounded representation of dementia progression, emphasizing structural connectome alterations as potential markers of cognitive decline. Full article

Phenylketonuria (PKU) is a monogenic disorder caused by pathogenic variants in the gene encoding phenylalanine hydroxylase (PAH), an enzyme essential for phenylalanine (Phe) metabolism. It is characterized by elevated Phe levels, leading to a wide spectrum of clinical phenotypes. These phenotypes are characterized by varying Phe accumulation, dietary tolerance, and heterogeneous cognitive and neurological outcomes, but current monitoring methods, focused primarily on blood Phe levels, are limited in capturing this variability. In this study, we applied mass spectrometry-based advanced quantitative amino acid analyses, untargeted metabolomics, and lipidomics analyses. We examined the plasma metabolite and lipid profiles in a total of 73 individuals with various PKU phenotypes against healthy controls to see how the metabolome and lipidome of the patients change in different phenotypes. We investigated whether novel markers could be associated with metabolic control status. By elucidating the metabolic and lipid fingerprints of PKU’s phenotypic variability, our findings may provide novel insights that could inform the refinement of dietary and pharmacological interventions, thereby supporting the development of more personalized treatment strategies. Full article

Background: Digital technologies offer innovative opportunities for recovering and maintaining intellectual and mental health. The use of a multitask approach that combines motor component with various cognitive tasks in a virtual environment can optimize cognitive and physical functions and improve the quality of life of cardiac surgery patients. This study aimed to localize current sources of theta and alpha power in patients who have undergone virtual multitask training (VMT) and a control group in the early postoperative period of coronary artery bypass grafting (CABG). Methods: A total of 100 male CABG patients (mean age, 62.7 ± 7.62 years) were allocated to the VMT group (n = 50) or to the control group (n = 50). EEG was recorded in the eyes-closed resting state at baseline (2–3 days before CABG) and after VMT course or approximately 11–12 days after CABG (the control group). Power EEG analysis was conducted and frequency-domain standardized low-resolution tomography (sLORETA) was used to assess the effect of VMT on brain activity. Results: After VMT, patients demonstrated a significantly higher density of alpha-rhythm (7–9 Hz) current sources (t > −4.18; p < 0.026) in Brodmann area 30, parahippocampal, and limbic system structures compared to preoperative data. In contrast, the control group had a marked elevation in the density of theta-rhythm (3–5 Hz) current sources (t > −3.98; p < 0.017) in parieto-occipital areas in comparison to preoperative values. Conclusions: Virtual reality-based multitask training stimulated brain regions associated with spatial orientation and memory encoding. The findings of this study highlight the importance of neural mechanisms underlying the effectiveness of multitask interventions and will be useful for designing and conducting future studies involving VR multitask training. Full article

Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration—exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems. Full article

Background/Objectives: Obsessive–compulsive disorder (OCD) is a complex psychiatric condition often associated with alterations in cognitive processes, including memory. Although memory dysfunction has been proposed as a contributing factor to the onset and maintenance of OCD symptoms, it remains debated whether these deficits reflect genuine cognitive impairments or maladaptive metacognitive processes, such as pathological doubt and memory distrust. This review aims to synthesize current findings on memory functioning in OCD, focusing on distinct memory systems and the role of metacognition. Methods: A comprehensive literature search was conducted across five databases (PubMed, Scopus, Embase, PsycINFO, and Google Scholar), covering studies up to April 2025. Search terms included “Obsessive-compulsive disorder”; “OCD”; “Memory dysfunction”; “Episodic memory”; “Working memory impairment”; “Prospective memory deficits”; “Checking compulsions”; “Memory confidence”; “Cognitive biases”. Results: Short-term memory appears generally preserved in OCD. Working memory deficits are consistently reported, especially in the visuospatial domain, and they are associated with difficulties in updating and clearing irrelevant information. Episodic memory impairments are common and often linked to inefficient encoding strategies and heightened cognitive self-consciousness. Prospective memory is frequently compromised under neutral conditions. Individuals with checking symptoms tend to show intact objective memory performance, despite reporting low memory confidence, supporting the concept of memory distrust. Conclusions: Memory dysfunction in OCD is multifaceted, involving both cognitive and metacognitive alterations. The evidence supports a model in which executive dysfunctions and memory-related beliefs contribute to compulsive behaviors more than objective memory failure. These insights highlight the need for integrative assessment protocols and personalized interventions targeting both cognitive performance and metacognitive appraisals. Full article

The present Perspective analyzes the remarkable evolution of the Amyloid Cascade Hypothesis 2.0 (ACH2.0) theory of Alzheimer’s disease (AD) since its inception a few years ago, as reflected in the diminishing role of amyloid-beta (Aβ) in the disease. In the initial iteration of the ACH2.0, Aβ-protein-precursor (AβPP)-derived intraneuronal Aβ (iAβ), accumulated to neuronal integrated stress response (ISR)-eliciting levels, triggers AD. The neuronal ISR, in turn, activates the AβPP-independent production of its C99 fragment that is processed into iAβ, which drives the disease. The second iteration of the ACH2.0 stemmed from the realization that AD is, in fact, a disease of the sustained neuronal ISR. It introduced two categories of AD—conventional and unconventional—differing mainly in the manner of their causation. The former is caused by the neuronal ISR triggered by AβPP-derived iAβ, whereas in the latter, the neuronal ISR is elicited by stressors distinct from AβPP-derived iAβ and arising from brain trauma, viral and bacterial infections, and various types of inflammation. Moreover, conventional AD always contains an unconventional component, and in both forms, the disease is driven by iAβ generated independently of AβPP. In its third, the current, iteration, the ACH2.0 posits that proteolytic production of Aβ is suppressed in AD-affected neurons and that the disease is driven by C99 generated independently of AβPP. Suppression of Aβ production in AD seems an oxymoron: Aβ is equated with AD, and the later is inconceivable without the former in an ingrained Amyloid Cascade Hypothesis (ACH)-based notion. But suppression of Aβ production in AD-affected neurons is where the logic leads, and to follow it we only need to overcome the inertia of the preexisting assumptions. Moreover, not only is the generation of Aβ suppressed, so is the production of all components of the AβPP proteolytic pathway. This assertion is not a quantum leap (unless overcoming the inertia counts as such): the global cellular protein synthesis is severely suppressed under the neuronal ISR conditions, and there is no reason for constituents of the AβPP proteolytic pathway to be exempted, and they, apparently, are not, as indicated by the empirical data. In contrast, tau protein translation persists in AD-affected neurons under ISR conditions because the human tau mRNA contains an internal ribosomal entry site in its 5′UTR. In current mouse models, iAβ derived from AβPP expressed exogenously from human transgenes elicits the neuronal ISR and thus suppresses its own production. Its levels cannot principally reach AD pathology-causing levels regardless of the number of transgenes or the types of FAD mutations that they (or additional transgenes) carry. Since the AβPP-independent C99 production pathway is inoperative in mice, the current transgenic models have no potential for developing the full spectrum of AD pathology. What they display are only effects of the AβPP-derived iAβ-elicited neuronal ISR. The paper describes strategies to construct adequate transgenic AD models. It also details the utilization of human neuronal cells as the only adequate model system currently available for conventional and unconventional AD. The final alteration of the ACH2.0, introduced in the present Perspective, is that AβPP, which supports neuronal functionality and viability, is, after all, potentially produced in AD-affected neurons, albeit not conventionally but in an ISR-driven and -compatible process. Thus, the present narrative begins with the “omnipotent” Aβ capable of both triggering and driving the disease and ends up with this peptide largely dislodged from its pedestal and retaining its central role in triggering the disease in only one, although prevalent (conventional), category of AD (and driving it in none). Among interesting inferences of the present Perspective is the determination that “sporadic AD” is not sporadic at all (“non-familial” would be a much better designation). The term has fatalistic connotations, implying that the disease can strike at random. This is patently not the case: The conventional disease affects a distinct subpopulation, and the basis for unconventional AD is well understood. Another conclusion is that, unless prevented, the occurrence of conventional AD is inevitable given a sufficiently long lifespan. This Perspective also defines therapeutic directions not to be taken as well as auspicious ways forward. The former category includes ACH-based drugs (those interfering with the proteolytic production of Aβ and/or depleting extracellular Aβ). They are legitimate (albeit inefficient) preventive agents for conventional AD. There is, however, a proverbial snowball’s chance in hell of them being effective in symptomatic AD, lecanemab, donanemab, and any other “…mab” or “…stat” notwithstanding. They comprise Aβ-specific antibodies, inhibitors of beta- and gamma-secretase, and modulators of the latter. In the latter category, among ways to go are the following: (1) Depletion of iAβ, which, if sufficiently “deep”, opens up a tantalizing possibility of once-in-a-lifetime preventive transient treatment for conventional AD and aging-associated cognitive decline, AACD. (2) Composite therapy comprising the degradation of C99/iAβ and concurrent inhibition of the neuronal ISR. A single transient treatment could be sufficient to arrest the progression of conventional AD and prevent its recurrence for life. Multiple recurrent treatments would achieve the same outcome in unconventional AD. Alternatively, the sustained reduction/removal of unconventional neuronal ISR-eliciting stressors through the elimination of their source would convert unconventional AD into conventional one, preventable/treatable by a single transient administration of the composite C99/iAβ depletion/ISR suppression therapy. Efficient and suitable ISR inhibitors are available, and it is explicitly clear where to look for C99/iAβ-specific targeted degradation agents—activators of BACE1 and, especially, BACE2. Directly acting C99/iAβ-specific degradation agents such as proteolysis-targeting chimeras (PROTACs) and molecular-glue degraders (MGDs) are also viable options. (3) A circumscribed shift (either upstream or downstream) of the position of transcription start site (TSS) of the human AβPP gene, or, alternatively, a gene editing-mediated excision or replacement of a small, defined segment of its portion encoding 5′-untranslated region of AβPP mRNA; targeting AβPP RNA with anti-antisense oligonucleotides is another possibility. If properly executed, these RNA-based strategies would not interfere with the protein-coding potential of AβPP mRNA, and each would be capable of both preventing and stopping the AβPP-independent generation of C99 and thus of either preventing AD or arresting the progression of the disease in its conventional and unconventional forms. The paper is interspersed with “validation” sections: every conceptually significant notion is either validated by the existing data or an experimental procedure validating it is proposed. Full article

Alzheimer’s disease, an irreversible neurodegenerative disorder, manifests through the progressive deterioration of memory and cognitive functions. While magnetic resonance imaging has become an indispensable neuroimaging modality for Alzheimer’s disease diagnosis and monitoring, current diagnostic paradigms predominantly rely on single-time-point data analysis, neglecting the inherent longitudinal nature of neuroimaging applications. Therefore, in this paper, we propose a multi-task feature selection algorithm for Alzheimer’s disease classification based on longitudinal imaging and hypergraphs (THM2TFS). Our methodology establishes a multi-task learning framework where feature selection at each temporal interval is treated as an individual task within each imaging modality. To address temporal dependencies, we implement group sparse regularization with two critical components: (1) a hypergraph-induced regularization term that captures high-order structural relationships among subjects through hypergraph Laplacian modeling, and (2) a fused sparse Laplacian regularization term that encodes progressive pathological changes in brain regions across time points. The selected features are subsequently integrated via multi-kernel support vector machines for final classification. We used functional magnetic resonance imaging and structural functional magnetic resonance imaging data from Alzheimer’s Disease Neuroimaging Initiative at four different time points (baseline (T1), 6th month (T2), 12th month (T3), and 24th month (T4)) to evaluate our method. The experimental results show that the accuracy rates of 96.75%, 93.45, and 83.78 for the three groups of classification tasks (AD vs. NC, MCI vs. NC and AD vs. MCI) are obtained, respectively, which indicates that the proposed method can not only capture the relevant information between longitudinal image data well, but also the classification accuracy of Alzheimer’s disease is improved, and it helps to identify the biomarkers associated with Alzheimer’s disease. Full article

Background: The human brain relies on complex synaptic communication regulated by key genes such as SYNGAP1. SYNGAP1 encodes the GTPase-Activating Protein (SYNGAP), a critical synaptic plasticity and neuronal excitability regulator. Impaired SYNGAP1 function leads to neurodevelopmental disorders (NDDs) characterized by intellectual disability (ID), epilepsy, and behavioral abnormalities. These variants disrupt Ras signaling, altering AMPA receptor transport and synaptic plasticity and contributing to cognitive and motor difficulties. Despite advancements, challenges remain in defining genotype–phenotype correlations and distinguishing SYNGAP1-related disorders from other NDDs, which could improve underdiagnosis and misdiagnosis. Brain Gene Registry: The Brain Gene Registry (BGR) was established as a collaborative initiative, consolidating genomic and phenotypic data across multiple research centers. This database allows for extensive analyses, facilitating improved diagnostic accuracy, earlier interventions, and targeted therapeutic strategies. The BGR enhances our understanding of rare genetic conditions and is critical for advancing research on SYNGAP1-related disorders. Conclusions: While no FDA-approved treatments exist for SYNGAP1-related disorders, several therapeutic approaches are being investigated. These include taurine supplementation, ketogenic diets, and molecular strategies such as antisense oligonucleotide therapy to restore SYNGAP1 expression. Behavioral and rehabilitative interventions remain key for managing developmental and cognitive symptoms. Advancing research through initiatives like the BGR is crucial for refining genotype–phenotype associations and developing precision medicine approaches. A comprehensive understanding of SYNGAP1-related disorders will improve clinical outcomes and patient care, underscoring the need for continued interdisciplinary collaboration in neurodevelopmental genetics. Full article

(1) Background: Individuals with high levels of state depression are hypothesized to have an impairment of attentional control functions necessary for filtering irrelevant information. This study used the event-related potential of early P_D, a marker of distractor suppression, and N2pc, an indicator of attentional capture to investigate whether high state depression affects selective attention in ignoring or suppressing distractors. (2) Methods: Thirty-three undergraduate students completed the Depression Anxiety Stress Scale-21 (DASS-21) and performed a modified, delayed match-to-sample task. Participants encoded abstract shapes under low or high perceptual load conditions in the visual working memory while ignoring a lateralized Chinese character as a task-irrelevant singleton distractor. (3) Results: Individuals with high state depression failed to suppress the distractor, as evidenced by the absence of early P_D. Under low perceptual loads, they also displayed a significant N2pc component, indicating attentional allocation to the distractor. In contrast, low-state-depression participants successfully suppressed the distractor, showing early P_D and the absence of N2pc. (4) Conclusions: These findings suggest that high-state-depression individuals have an impairment in top–down attentional control, particularly in feature-based selective attention. This deficit hinders the ability to filter out irrelevant information, potentially contributing to cognitive difficulties associated with depression. Full article

FKBP5 has been of special scientific interest in the behavioral sciences since it has been involved in the pathophysiology of several mental disorders. It is a gene with pleiotropic effects which encodes the protein FKBP5, a cochaperone that decreases glucocorticoid receptor (GR) affinity for glucocorticoids by competing with FKBP4, altering the GR chaperone complex, and impairing GR activation. As a key modulator of the stress response, FKBP5 plays a critical role in regulating cortisol levels in the organism. The FKBP5 gene is regulated through a combination of transcriptional, epigenetic, post-transcriptional, and environmental mechanisms, as well as genetic polymorphisms that influence its transcription and stress responsiveness. Notably, the rs1360780 T-allele in FKBP5 significantly affects FKBP5 regulation and has been linked to stress-related disorders by influencing transcription and stress responsiveness. In this narrative review, we aim to provide an overview of the role played by the single-nucleotide polymorphism rs1360780 in the FKBP5 locus in gene expression, its epigenetic regulation, and the impact of early stress in its functioning. We discuss some brain regions with differential expression of FKBP5 and some behavioral phenotypes linked to the locus. The T-allele of rs1360780 is considered a risk variant, as it leads to high FKBP5 induction, which delays negative feedback and increases GR resistance. This results in states of relative hypercortisolemia and brain morphofunctional alterations, particularly in regions sensitive to glucocorticoid activity during critical periods of neurodevelopment. Additionally, exposure to childhood maltreatment is associated with demethylation of the glucocorticoid response elements of FKBP5, further increasing its expression levels. Among the psychological dimensions analyzed in which FKBP5 is involved are neurocognition, aggression, suicidality, and social cognition. At the level of mental disorders, the gene may play a role in the pathogenesis of post-traumatic stress disorder, depression, and bipolar disorder. In psychotic disorders, its role is less clear. This knowledge enhances the understanding of disease mechanisms that operate through psychopathological dimensions, and highlights the need to design specific, person-centered psychopharmacological and environmental therapeutic interventions. Full article