Biology subjects

Torres, E. B.

Publications and source records attributed to Torres, E. B..

12 recordsLinked to original sources

bioRxiv2025

Facial Micro-Movements as a Proxy of Increasingly Erratic Heart Rate Variability While Experiencing Pressure Pain

The sensation of pain varies from person to person. These patterns of individual variations are difficult to capture using coarse subjective self-reports. However, they are important when prescribing therapies and tailoring them to the persons own sensations. Pain can be experienced differently by the same person, and fluctuate differently depending on the context, yet most analyses treat the problem under a one-size-fits-all model. In this work, we introduce a series of assays to objectively assess pressure pain across tasks with different motoric and cognitive demands, in relation to resting state. In a cohort of healthy individuals, we examine pain-free vs. pain states at rest, during drawing with heavy cognitive demands, during pointing to a visual target, and during a grooved peg task like inserting a grooved key in a matching grooved keyhole. We recorded Face videos, electrocardiographic signals and adopt a standardized data type called the micro-movement spikes (MMS) to characterize the biorhythmic activities of the Face micro-expressions and of the micro-fluctuations in the hearts inter beat interval timings. Using the MMS peaks, we find that the continuous Gamma family of probability distribution functions best fit the frequency histograms of both the Face and the heart data. Further, we find that the Gamma shape and scale parameters in both signals span a scaling power law whereby as the noise- to-signal ratio (Gamma scale parameter) increases, so does the randomness of the stochastic process (the Gamma shape decreases towards the memoryless exponential range). We find that as the heart IBI turns more erratic (noisier and more random) the facial ophthalmic region increases the noise and randomness too, with higher linear correlation for tasks requiring haptic feedback (R2 0.84) than for tasks requiring higher cognitive and memory loads (R2 0.77). Increases in transfer entropy shows that recent past activity ([~]167ms back) of the heart IBI and Face combined lower the uncertainty in the prediction of the present ophthalmic-Face activity, suggesting that this Face region may serve as a proxy of an increasingly dysregulated heart. These results bear implications for the detection and monitoring of pressure pain and heart dysregulated states. Scope StatementThis work uncovers personalized thresholds of pain through the combination of the participants fluctuations in HRV and facial micro-expressions. These signals are obtained before and after experiencing the sensation of pressure pain compared to a pain-free control condition. Tasks with higher haptic demand evoke higher differentiation in the signals than tasks with higher cognitive and memory loads. As the detection and tracking of such individual thresholds is possible through these unobtrusive and highly scalable methods, the work has implications for the personalized tailoring of pain treatments beyond the realms of the lab.

neuroscience↗

bioRxiv2023

Irregularity of instantaneous gamma frequency in the motor control network characterize visuomotor and proprioceptive information processing

BackgroundGoal-directed movements involve integrating proprioceptive and visuo-motor information. Although the neural correlates of such information processing are known, the details of how sensory-motor integration occurs are still largely unknown. ObjectiveThe study aims to characterize movements with different sensory goals, by contrasting the neural activity involved in processing proprioceptive and visuo-motor information. To accomplish this, we have developed a new methodology that utilizes the irregularity of the instantaneous gamma frequency parameter for characterization. ApproachIn this study, 8 essential tremor patients undergoing an awake deep brain stimulation (DBS) implantation surgery repetitively touched the clinicians finger (forward visually-guided/FV movement) and then ones own chin (backward proprioceptively-guided/BP movement). Neural electrocorticographic (ECoG) recordings from the motor (M1), somatosensory (S1), and posterior parietal cortex (PPC) were obtained and band-pass filtered in the gamma range (30-80Hz). The irregularity of the inter-event intervals (IEI; inverse of instantaneous gamma frequency) were examined as: 1) correlation between the amplitude and its proceeding IEI, and 2) auto-information of the IEI time series. We further explored the network connectivity after segmenting the FV and BP movements by periods of accelerating and decelerating forces, and applying the IEI parameter to transfer entropy methods. ResultsConceptualizing that the irregularity in IEI reflects active new information processing, we found the highest irregularity in M1 during BP movement, highest in PPC during FV movement, and the lowest during rest at all sites. Also, connectivity was the strongest from S1 to M1 and from S1 to PPC during FV movement with accelerating force and weakest during rest. SignificanceWe introduce a novel methodology that utilize the instantaneous gamma frequency (i.e., IEI) parameter in characterizing goal-oriented movements with different sensory goals, and demonstrate its use to inform the directional connectivity within the motor cortical network. This method successfully characterizes different movement types, while providing interpretations to the sensory-motor integration processes.

neuroscience↗

bioRxiv2022

Toward Interpretable Digital Biomarkers of Walking and Reaching in Parkinson's Disease

Multimodal digital data registered with wearable biosensors has emerged as highly complementary of clinical pencil-and-paper criteria, offering new insights in ways to detect and diagnose various aspects of Parkinsons disease. A pressing question is how to combine both the clinical knowledge of PD and the new technology to create interpretable digital biomarkers easily obtainable with off-the-shelf technology. Several challenges concerning disparity in biophysical units, anatomical differences across participants, sensor positioning, and sampling resolution are addressed in this work, along with identification of optimal parameters to automatically differentiate patients with PD from controls. We combine data from a multitude of biosensors registering signals from the central (EEG) and peripheral (magnetometry, kinematics) nervous systems, inclusive of the autonomic nervous system (EKG), as the participants perform natural tasks requiring different levels of intentional planning and automatic control. We find that magnetometer data during walking, across a variety of amplitude and timing signals provide optimal separation of PD from neurotypical controls. We conclude that considering multimodal signals, while differentiating across levels of intent in natural actions can be revealing of important features of PD that otherwise escape the naked eye. Further we add that clinical criteria combined with such optimal digital parameter spaces offer a far more complete picture of PD than using either one of these pieces of data alone.

neuroscience↗

bioRxiv2022

Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Neuropsychiatric Disorders

1The advent of open access to genomic data offers new opportunities to revisit old clinical debates while approaching them from a different angle. We examine anew the question of whether psychiatric and neurological disorders are different from each other by assessing the pool of genes associated with disorders that are understood as psychiatric or as neurological. We do so in the context of transcriptome data tracked as human embryonic stem cells differentiate and become neurons. Building upon probabilistic layers of increasing complexity, we describe the dynamics and stochastic trajectories of the full transcriptome and the embedded genes associated with psychiatric and/or neurological disorders. From marginal distributions of a genes expression across hundreds of cells, to joint interactions taken globally to determine degree of pairwise dependency, to networks derived from probabilistic graphs along maximal spanning trees, we have discovered two fundamentally different classes of genes underlying these disorders and differentiating them. One class of genes boasts higher variability in expression and lower dependencies ("active genes"); the other has lower variability and higher dependencies ("lazy genes"). They give rise to different network architectures and different transitional states. Active genes have large hubs and a fragile topology, whereas lazy genes show more distributed code during the maturation toward neuronal state. Lazy genes boost differentiation between psychiatric and neurological disorders also at the level of tissue across the brain, spinal cord, and glands. These genes, with their low variability and asynchronous ON/OFF states that have been treated as gross data and excluded from traditional analyses, are helping us settle this old argument at more than one level of inquiry. 2 Manuscript Contribution to the FieldThere is an ongoing debate on whether psychiatric disorders are fundamentally different from neurological disorders. We examine this question anew in the context of transcriptome data tracked as human embryonic stem cells differentiate and become neurons. Building upon probabilistic layers of increasing complexity, we describe the dynamics and stochastic trajectories of the full transcriptome and the embedded genes associated with psychiatric and/or neurological disorders. Two fundamentally different types of genes emerge: "lazy genes" with low, odd, and asynchronous variability patterns in expression that would have been, under traditional approaches, considered superfluous gross data, and "active genes" likely included under traditional computational techniques. They give rise to different network architectures and different transitional dynamic states. Active genes have large hubs and a fragile topology, whereas lazy genes show more distributed code during the maturation toward neuronal state. Under these new wholistic approach, the methods reveal that the lazy genes play a fundamental role in differentiating psychiatric from neurological disorders across more than one level of analysis. Including these genes in future interrogation of transcriptome data may open new lines of inquiry across brain genomics in general.

genomics↗

bioRxiv2022

Sensing Echoes: Temporal misalignment as the Earliest Marker of Neurodevelopmental Derail

Neurodevelopmental disorders are on the rise worldwide, with diagnoses that detect derailment from typical milestones by 3-4.5 years of age. By then, the circuitry in the brain has already reached some level of maturation that inevitably takes neurodevelopment through a different course. There is a critical need then to develop analytical methods that detect problems much earlier and identify targets for treatment. We integrate data from multiple sources, including neonatal auditory brainstem responses (ABR), clinical criteria detecting autism years later in those neonates, and similar ABR information for young infants and children who also received a diagnosis of autism spectrum disorders, to produce the earliest known digital screening biomarker to flag neurodevelopmental derailment in neonates. This work also defines concrete targets for treatment and offers a new statistical approach to aid in guiding a personalized course of maturation in line with the highly nonlinear, accelerated neurodevelopmental rates of change in early infancy. Significance StatementAutism is currently detected on average after 4.5 years of age, based on differences in social interactions. Yet basic building blocks that develop to scaffold social interactions are present at birth and quantifiable at clinics. Auditory Brainstem Response tests, routinely given to neonates, infants, and young children, contain information about delays in signal transmission important for sensory integration. Although currently discarded as gross data under traditional statistical approaches, new analytics reveal unambiguous differences in ABR signals fluctuations between typically developing neonates and those who received an autism diagnosis. With very little effort and cost, these new analytics could be added to the clinical routine testing of neonates to create a universal screening tool for neurodevelopmental derailment and prodrome of autism.

neuroscience↗

bioRxiv2022

Rethinking Statistical Learning as a Dynamic Stochastic Process, from The Motor Systems Perspective

The brain integrates streams of sensory input and builds accurate predictions, while arriving at stable percepts under disparate time scales. This stochastic process bears different dynamics for different people, yet statistical learning (SL) currently averages out, as noise, individual fluctuations in data streams registered from the brain as the person learns. We here adopt the motor systems perspective to reframe SL. Specifically, we rethink this problem using the demands that the persons brain faces to predict, and control variations in biorhythmic activity akin to those present in bodily motions. This new approach harnesses gross data as the important signals, to reassess how individuals learn predictive information in stable and unstable environments. We find two types of learners: narrow-variance learners, who retain explicit knowledge of the regularity embedded in the stimuli -the goal. They seem to use an error-correction strategy steadily present in both stable and unstable cases. In contrast, broad-variance learners emerge only in the unstable environment. They undergo an initial period of memoryless learning characterized by a gamma process that starts out exponentially distributed but converges to Gaussian. We coin this mode exploratory, preceding the more general error-correction mode characterized by skewed-to-symmetric distributions and higher signal content from the start. Our work demonstrates that statistical learning is a highly dynamic and stochastic process, unfolding at different time scales, and evolving distinct learning strategies on demand.

neuroscience↗

bioRxiv2021

Precision Autism: Genomic Stratification of Disorders Making Up the Broad Spectrum May Demystify its "Epidemic Rates"

In the last decade, Autism has broadened and often shifted its diagnostics criter a, allowing several neuropsychiatric and neurological disorders of known etiology. This has resulted in a highly heterogeneous spectrum with apparent exponential rates in prevalence. I ask if it is possible to leverage existing genetic information about those disorders making up Autism today and use it to stratify this spectrum. To that end, I combine genes linked to Autism in the SFARI database and genomic information from the DisGeNet portal on 25 diseases, inclusive of non-neurological ones. I use the GTEx data on genes expression on 54 human tissues and ask if there are overlapping genes across those associated to these diseases and those from SFARI-Autism. I find a compact set of genes across all brain-disorders which express highly in tissues fundamental for somatic-sensory-motor function, self-regulation, memory, and cognition. Then, I offer a new stratification that provides a distance-based orderly clustering into possible Autism subtypes, amenable to design personalized targeted therapies within the framework of Precision Medicine. I conclude that viewing Autism through this physiological (Precision) lens, rather than viewing it exclusively from a psychological behavioral construct, may make it a more manageable condition and dispel the Autism epidemic myth.

genomics↗

bioRxiv2021

Optimal Time Lags from Causal Prediction Model Help Stratify and Forecast Nervous System Pathology

Traditional clinical approaches diagnose disorders of the nervous system using standardized observational criteria. Although aiming for homogeneity of symptoms, this method often results in highly heterogeneous disorders. A standing question thus is how to automatically stratify a given random cohort of the population, such that treatment can be better tailored to each clusters symptoms, and severity of any given group forecasted to provide neuroprotective therapies. In this work we introduce new methods to automatically stratify a random cohort of the population composed of healthy controls of different ages and patients with different disorders of the nervous systems. Using a simple walking task and measuring micro-fluctuations in their biorhythmic motions, we combine non-linear causal network connectivity analyses in the temporal and frequency domains with stochastic mapping. The methods define a new type of internal motor timings. These are amenable to create personalized clinical interventions tailored to self-emerging clusters signaling fundamentally different types of gait pathologies. We frame our results using the principle of reafference and operationalize them using causal prediction, thus renovating the theory of internal models for the study of neuromotor control.

neuroscience↗

bioRxiv2020

Reframing Psychiatry for Precision Medicine

The art of observing and describing behaviors has driven diagnosis and informed basic science in Psychiatry. In recent times, studies of mental illness are focused on understanding the brains neurobiology but there is a paucity of information on the potential contributions from peripheral activity to mental health. In Precision Medicine, this common practice leaves a gap between bodily behaviors and genomics that we here propose to address with a new layer of inquiry that includes genes expression on tissues inclusive of brain, heart, muscle-skeletal and organs for vital bodily functions. We interrogate genes expression on human tissue as a function of disease-associated genes. By removing genes linked to disease from the typical human set, and recomputing the genes expressions on the tissues, we can compare the outcomes across mental illnesses, well-known neurological conditions, and non-neurological ones. We find that major neuropsychiatric conditions that are behaviorally defined today (e.g. Autism, Schizophrenia, Depression) through DSM-observation criteria, have strong convergence with well-known neurological ones (e.g. Ataxias, Parkinson), but less overlap with non-neurological ones. Surprisingly, tissues majorly involved in the central control, coordination, adaptation and learning of movements, emotion and memory are maximally affected in psychiatric diagnoses along with peripheral heart and muscle-skeletal tissues. Our results underscore the importance of considering both the brain-body connection and the contributions of the peripheral nervous systems to mental health.

genomics↗

bioRxiv2020

Roadmap to Help Develop Personalized Targeted Treatments for Autism as a Disorder of the Nervous Systems

There is a disconnect between the clinical behavioral definition of autism and the genomic science that this definition largely informs and steers. But the digital sensor revolution paired with open access to genomics data has the potential to bridge the gap between these two layers of knowledge. Here we use the SFARI genes module and interrogate the human genome upon removing those genes. We then compare the remaining genes expression on tissues responsible for brain, heart and organs function to its counterpart in well-known neurological disorders of genetic origins. Despite clinical criteria emphasizing a behavioral definition of Autism, over a neurological one, here we find convergence between Autism and the neurological disorders. Tissues involved in motor control, emotions and memory are the most affected by the removal of the SFARI Autism genes. Congruent with this picture, the Ataxias, Parkinsons disease and Fragile X share 76.9% of the most affected tissues, including those related to motor control and autonomic function, while mitochondria disorder share 61.5% with autism. Together, these results offer a new roadmap to help diagnosis and personalized targeted treatments of autism. They underscore Autism as an objectively quantifiable disorder of the nervous systems.

genomics↗

bioRxiv2020

The Autonomic Nervous System Differentiates Between Levels of Motor Intent and Hand Dominance

While attempting to bridge motor control and cognitive science, the nascent field of embodied cognition has primarily addressed intended, goal-oriented actions. Less explored however, have been unintended motions. Such movements tend to occur largely beneath awareness, while contributing to the spontaneous control of redundant degrees of freedom across the body in motion. We posit that the consequences of such unintended actions implicitly contribute to our autonomous sense of action ownership and agency. We question whether biorhythmic activities from these motions are separable from those which intentionally occur. Here we find that fluctuations in the biorhythmic activities of the nervous systems can unambiguously differentiate across levels of intent. More important yet, this differentiation is remarkable when we examine the fluctuations in biorhythmic activity from the autonomic nervous systems. We find that when the action is intended, the heart signal leads the body kinematics signals; but when the action segment spontaneously occurs without instructions, the heart signal lags the bodily kinematics signals. We posit that such differentiation within the nervous system, may be necessary to acquire the sense of action ownership, which in turn, contributes to the sense of agency. We discuss our results while considering their potential translational value.

neuroscience↗

bioRxiv2019

Hidden Aspects of the Research-ADOS are Bound to Affect Autism Science

The research-grade ADOS is a broadly used instrument that informs and steers much of the science of Autism. Despite its broad use, little is known about the empirical variability inherently present in the scores of the ADOS scale, or their appropriateness to define change, to repeatedly use this test to characterize neurodevelopmental trajectories. Here we examine the empirical distributions of research-grade ADOS scores from 1,324 records in a cross-section of the population comprising participants with autism between 5-65 years of age. We find that these empirical distributions violate the theoretical requirements of normality and homogeneous variance, essential for independence between bias and sensitivity. Further, we assess a subset of 52 typical controls vs. those with autism and find lack of proper elements to characterize neurodevelopmental trajectories in a coping nervous system changing at non-uniform, non-linear rates. Lastly, longitudinally repeating the assessments over 4 visits in a subset of the participants with autism for whom verbal criteria kept the same appropriate ADOS modules over the timespan of the 4 visits, reveals that switching the clinician, changes the cutoff scores, and consequently, influences the diagnosis, despite maintaining fidelity in the same tests modules, room conditions and tasks fluidity per visit. Given the changes in probability distribution shape and dispersion of these ADOS scores, the lack of appropriate metric spaces, and the impact that these elements have on sensitivity-bias co-dependencies, and on longitudinal tracking of autism, we invite a discussion on the use of this test for scientific purposes.

neuroscience↗