30 research outputs found
Deep generative modelling of the imaged human brain
Human-machine symbiosis is a very promising opportunity for the field of neurology given that the interpretation of the imaged human brain is a trivial feat
for neither entity. However, before machine learning systems can be used in
real world clinical situations, many issues with automated analysis must first be
solved. In this thesis I aim to address what I consider the three biggest hurdles
to the adoption of automated machine learning interpretative systems. For each
issue, I will first elucidate the reader on its importance given the overarching
narratives of both neurology and machine learning, and then showcase my proposed solutions to these issues through the use of deep generative models of the
imaged human brain.
First, I start by addressing what is an uncontroversial and universal sign of intelligence: the ability to extrapolate knowledge to unseen cases. Human neuroradiologists have studied the anatomy of the healthy brain and can therefore,
with some success, identify most pathologies present on an imaged brain, even
without having ever been previously exposed to them. Current discriminative
machine learning systems require vast amounts of labelled data in order to accurately identify diseases. In this first part I provide a generative framework that
permits machine learning models to more efficiently leverage unlabelled data for
better diagnoses with either none or small amounts of labels.
Secondly, I address a major ethical concern in medicine: equitable evaluation
of all patients, regardless of demographics or other identifying characteristics.
This is, unfortunately, something that even human practitioners fail at, making
the matter ever more pressing: unaddressed biases in data will become biases
in the models. To address this concern I suggest a framework through which
a generative model synthesises demographically counterfactual brain imaging
to successfully reduce the proliferation of demographic biases in discriminative
models.
Finally, I tackle the challenge of spatial anatomical inference, a task at the centre
of the field of lesion-deficit mapping, which given brain lesions and associated
cognitive deficits attempts to discover the true functional anatomy of the brain.
I provide a new Bayesian generative framework and implementation that allows
for greatly improved results on this challenge, hopefully, paving part of the road
towards a greater and more complete understanding of the human brain
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Limited-memory warping LCSS for real-time low-power pattern recognition in wireless nodes
We present and evaluate a microcontroller-optimized limited-memory implementation of a Warping Longest Common Subsequence algorithm (WarpingLCSS). It permits to spot patterns within noisy sensor data in real-time in resource constrained sensor nodes. It allows variability in the sensed system dynamics through warping; it uses only integer operations; it can be applied to various sensor modalities; and it is suitable for embedded training to recognize new patterns. We illustrate the method on 3 applications from wearable sensing and activity recognition using 3 sensor modalities: spotting the QRS complex in ECG, recognizing gestures in everyday life, and analyzing beach volleyball. We implemented the system on a low-power 8-bit AVR wireless node and a 32-bit ARM Cortex M4 microcontroller. Up to 67 or 140 10-second gestures can be recognized simultaneously in real-time from a 10Hz motion sensor on the AVR and M4 using 8mW and 10mW respectively. A single gesture spotter uses as few as 135μW on the AVR. The method allows low data rate distributed in-network recognition and we show a 100 fold data rate reduction in a complex activity recognition scenario. The versatility and low complexity of the method makes it well suited as a generic pattern recognition method and could be implemented as part of sensor front-ends
Deep Variational Lesion-Deficit Mapping
Causal mapping of the functional organisation of the human brain requires
evidence of \textit{necessity} available at adequate scale only from
pathological lesions of natural origin. This demands inferential models with
sufficient flexibility to capture both the observable distribution of
pathological damage and the unobserved distribution of the neural substrate.
Current model frameworks -- both mass-univariate and multivariate -- either
ignore distributed lesion-deficit relations or do not model them explicitly,
relying on featurization incidental to a predictive task. Here we initiate the
application of deep generative neural network architectures to the task of
lesion-deficit inference, formulating it as the estimation of an expressive
hierarchical model of the joint lesion and deficit distributions conditioned on
a latent neural substrate. We implement such deep lesion deficit inference with
variational convolutional volumetric auto-encoders. We introduce a
comprehensive framework for lesion-deficit model comparison, incorporating
diverse candidate substrates, forms of substrate interactions, sample sizes,
noise corruption, and population heterogeneity. Drawing on 5500 volume images
of ischaemic stroke, we show that our model outperforms established methods by
a substantial margin across all simulation scenarios, including comparatively
small-scale and noisy data regimes. Our analysis justifies the widespread
adoption of this approach, for which we provide an open source implementation:
https://github.com/guilherme-pombo/vae_lesion_defici
The legibility of the imaged human brain
Our knowledge of the organisation of the human brain at the population-level
is yet to translate into power to predict functional differences at the
individual-level, limiting clinical applications, and casting doubt on the
generalisability of inferred mechanisms. It remains unknown whether the
difficulty arises from the absence of individuating biological patterns within
the brain, or from limited power to access them with the models and compute at
our disposal. Here we comprehensively investigate the resolvability of such
patterns with data and compute at unprecedented scale. Across 23810 unique
participants from UK Biobank, we systematically evaluate the predictability of
25 individual biological characteristics, from all available combinations of
structural and functional neuroimaging data. Over 4526 GPU*hours of
computation, we train, optimize, and evaluate out-of-sample 700 individual
predictive models, including multilayer perceptrons of demographic,
psychological, serological, chronic morbidity, and functional connectivity
characteristics, and both uni- and multi-modal 3D convolutional neural network
models of macro- and micro-structural brain imaging. We find a marked
discrepancy between the high predictability of sex (balanced accuracy 99.7%),
age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute
error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance,
and the surprisingly low predictability of other characteristics. Neither
structural nor functional imaging predicted individual psychology better than
the coincidence of common chronic morbidity (p<0.05). Serology predicted common
morbidity (p<0.05) and was best predicted by it (p<0.001), followed by
structural neuroimaging (p<0.05). Our findings suggest either more informative
imaging or more powerful models will be needed to decipher individual level
characteristics from the brain.Comment: 36 pages, 6 figures, 1 table, 2 supplementary figure
The minimal computational substrate of fluid intelligence
The quantification of cognitive powers rests on identifying a behavioural
task that depends on them. Such dependence cannot be assured, for the powers a
task invokes cannot be experimentally controlled or constrained a priori,
resulting in unknown vulnerability to failure of specificity and
generalisability. Evaluating a compact version of Raven's Advanced Progressive
Matrices (RAPM), a widely used clinical test of fluid intelligence, we show
that LaMa, a self-supervised artificial neural network trained solely on the
completion of partially masked images of natural environmental scenes, achieves
human-level test scores a prima vista, without any task-specific inductive bias
or training. Compared with cohorts of healthy and focally lesioned
participants, LaMa exhibits human-like variation with item difficulty, and
produces errors characteristic of right frontal lobe damage under degradation
of its ability to integrate global spatial patterns. LaMa's narrow training and
limited capacity -- comparable to the nervous system of the fruit fly --
suggest RAPM may be open to computationally simple solutions that need not
necessarily invoke abstract reasoning.Comment: 26 pages, 5 figure
Body weight and mood state modifications in mixed martial arts: An exploratory pilot
Brandt, R, Bevilacqua, GG, Coimbra, DR, Pombo, LC, Miarka, B, and Lane, AM. Body weight and mood state modifications in mixed martial arts: An exploratory pilot. J Strength Cond Res 32(9): 2548-2554, 2018-Mixed martial arts (MMA) fighters typically use rapid weight loss (RWL) as a strategy to make competition weight. The aim of the present study was to compare body weight and mood changes in professional male MMA athletes who used strategies to rapidly lose weight (n = 9) and with MMA athletes who did not (n = 3). Body mass and mood states of anger, confusion, depression, fatigue, tension, and vigor and total mood disturbance were assessed (a) 30 days before competition, (b) at the official weigh-in 1 day before competition, (c) 10 minutes before competition, and (d) 10 minutes postcompetition. Results indicated that RWL associated with reporting higher confusion and greater total mood disturbance at each assessment point. Rapid weight loss also associated with high anger at the official weigh-in. However, in performance, RWL did not have deleterious effects on performance. The RWL group also reported greater total mood disturbance at all assessment points with a moderate difference effect size. Research supports the notion that RWL associates with potentially dysfunctional mood states
Mapping density, diversity and species-richness of the Amazon tree flora
Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution