Introduction
The prevalence of dementia is increasing globally and carries a growing personal and
societal burden (Guerchet et al., 2013). Multimodal and longitudinal neuroimaging
provides biomarkers about disease progression and informs early detection of dementia
(Ten Kate et al., 2018). However, current empirical data is still insufficient to
infer the underlying mechanisms of the disorder necessary for developing targeted
therapeutics. Equally important to the lack of empirical data, there is an absence
of sufficient theoretical tools to investigate the relationships among the genetic
risks, neuropathophysiology, clinical symptoms and environmental factors in neurodegenerative
diseases.
We argue that the recent advances in computational psychiatry and computational neurology
offer a promising translational neuroscience framework for integrating multiple levels
of abstractions and investigating neurobiological and pathological mechanisms of dementia.
In addition, they can derive mechanistic models that predict disease trajectory and
treatment effects. Here, we extend historical discussions on this topic (Adams et
al., 2016; Paulus et al., 2016; Hitchcock et al., 2022) by discussing the potential
of integrative computational modeling for dementia research. We will discuss the potential
translational benefits and how it might account for some of the current limitations
in dementia research.
Computational Psychiatry and Computational Neurology
With the rise of computational and data sciences applications in biological, medical,
biomedical, and psychological disciplines since the early 2010s, computational psychiatry
and computational neurology have demonstrated the potential to help account for some
of the limitations of traditional techniques (Montague et al., 2012). Computational
psychiatry and neurology are interwind and overlap in disorders like dementia, so
in the context of this paper, we do not distinguish them.
There are several different dichotomies of computational psychiatry and neurology
models (e.g., descriptive vs. predictive, discriminative vs. generative, exploratory
vs. confirmatory models). We recognize that all these dichotomies have their value
in describing specific classes of computational models. However, in this paper, we
would like to make a distinction between two types of scientific research approaches
by their different objectives: data-driven vs. theory-driven approaches. We argue
that the former primarily aims to explain patterns in novel data or information “about”
the diseased brain, while the latter primarily aims to develop, validate, or falsify
theories which describe information of the brain.
Data-Driven Computational Approaches
The primary objective of data-driven approaches is to “label” experimental data based
on multivariate patterns or statistical regularities in the data (e.g., by using machine
learning). An advantage of data-driven approaches is that they require minimal prior
assumptions about the data (Magoulas and Pentza, 1999). However, this very characteristic
also makes the interpretation of data-driven models challenging as discussed by Goecks
and colleagues (Goecks et al., 2020).
The current applications of machine learning in dementia research focus on disease
detection and prediction. For example, by using neuroimaging, biological, and clinical
data based on recurrent neural networks, support vector machines, decision trees,
Naïve Bayes classifiers, clustering, or other methods (Cui and Liu, 2019; Kuan et
al., 2021; Skolariki et al., 2021). Moreover, the efforts were made to develop a personalized
dementia risk model that could predict the onset of dementia years before patients
develop symptoms by using ensemble learning from demographic and medical history data
(Danso et al., 2021). Data-driven applications were also used to discriminate different
types of dementia (Dauwan et al., 2016; Bougea et al., 2021) or to decrease the number
of measures necessary for diagnosis (Weakley et al., 2015). Other computer-aided diagnosis
systems that automatically detect neurological abnormalities have been developed for
the identification of dementia from neuroimaging data (Siuly and Zhang, 2016). However,
translating these efforts into clinical practice is still problematic and need more
of easily used real-time methods that can be incorporated into everyday clinical practice.
Although, some examples that directly aim to model disease mechanisms, neuropathology,
or subtypes exist (Young et al., 2014; Oxtoby et al., 2018; Su et al., 2018, 2021),
majority of mainstream data-driven approaches do not explicitly intend to capture
the neurobiological and neuropathological mechanisms underlying dementia. While applicable
for disease prediction and diagnosis, data-driven approaches alone are still limited
to inform novel treatments and capture the underlying complexities of dynamic nature
of dementia (i.e., interactions of multiple disease factors on different levels that
can evolve in complex ways over time).
Theory-Driven Computational Approaches
Theory-driven approaches for computational psychiatry and neurology are used to describe
the mechanisms of altered pathology or information processing related to the “cause”
of psychiatric or neurological conditions. They are used as tools for characterizing
what nervous systems do (i.e., descriptive models), determining how they function
(i.e., mechanistic models), and understanding why they operate in particular ways
(i.e., interpretive models) at multiple levels of abstraction (Dayan and Abbott, 2001).
Thus, their goal is fundamentally different from data-driven approaches as they “force”
us to seek mechanisms and causality (Figure 1A).
Figure 1
Visual summary of proposed integrated computational modeling. (A) Data-driven and
theory-driven pipelines. (B) Different levels of abstraction. (C) Proposed integrated
computational modeling with examples.
First, biophysical models of synaptic, cellular, and neural circuits aim to describe
the association between psychiatric symptoms and abnormal information processing intrinsic
to assemblies of neurons and microcircuit dysfunctions. These models aim to investigate
mechanisms underlying cognitive decline and dementia. For instance, early work like
the “synaptic deletion and compensation” model (Horn et al., 1993; Ruppin and Reggi,
1995) demonstrated that synaptic connections in Alzheimer's disease are associated
with memory loss and learning difficulties. Second, large-scale neural network models
address the links between psychiatric problems and information processing dysfunction
intrinsic to large circuit functions (e.g., Raj and Itturia-Medina, 2019). Third,
normative models address how the nervous system should behave and how certain behavior
or neural activity deviates from those standards (e.g., “Perception and Attention
Deficit model”; Collerton et al., 2005; Makin et al., 2013).
A concern with many theory-driven models is that they are often based upon mechanisms
that are not directly accessible from experimental data (Moran et al., 2011) and provide
very specific assumptions that do not lead to empirically testable predictions (Baker
et al., 2018). Hence, methods that can bridge the gap between modeling the clinically
relevant symptoms (at macro-level) and modeling the brain where the neurobiological
mechanisms are implemented (at micro-level) are urgently needed. Here, we argue that
intermedium level models would complement macro- and micro-level models, being specifically
targeted at “meso-level” modeling. This allows for directly represented distributed
control of neural mechanisms and neurobiologically detailed cellular functions.
At this meso-level, the model has sufficient complexity to prescribe the hierarchical
architecture of the brain (i.e., a layer of units rather a single unit representing
a brain region); while each layer still can implement macro-level distributed representations
for perception, action, and language. Each unit in the model also includes micro-level
biological details (e.g., membrane potentials, ion channels, neurotransmitters) allowing
empirical validation such as by using neuroimaging. This has the potential to extend
traditional models that are predominantly informative on only one level of abstraction
(Figure 1B).
Examples of such models include our work on modeling attentional impairments in Alzheimer's
disease and making predictions about possible electrophysiological features in the
relevant neural circuits (Mavritsaki et al., 2019). These models can be seen as “virtual”
patients capturing the cognitive dysfunctions on computer simulations. By testing
the models with neuroimaging, biological and clinical data from real patients, we
can obtain mechanistic understanding and develop new drug treatments in-silico before
they are experimented on animals and humans. This can further speed up translational
effects and drug development in an area of great unmet need, reduce socio-economic
impact, and add to sustainability efforts in dementia research.
Combining Data-Driven and Theory-Driven Computational Approaches
Combining theory-driven and data-driven approaches in a single modeling framework
has the potential to account for the complexity of different forms of dementia while
considering overlapping pathologies and clinical symptoms (Figure 1C). This is particularly
crucial for: (I.) providing the understanding of the underlying mechanisms and “causal”
interactions obtained by theoretical models, and for (II.) overcoming the current
scalability limitations of theoretical models (Baker et al., 2018). For example, Maia
and Frank (2011) used reinforcement learning to quantify the learning ability of individuals
with Parkinson's disease based on the underlying dopaminergic mechanisms. Pinaya et
al. (2021) used deep learning on normative models of brain structure to detect Alzheimer's
disease progression. Bayesian models such as Dynamic Causal Modeling (DCM; Friston
et al., 2016) can be applied to neuroimaging data to describe effective connectivity
within and among neural circuits providing a principled data-driven way to fit subsets
of model parameters in theory-driven models.
Theory-driven models provide prior knowledge and context for estimating features specifically
relevant to disorders. This enables data-driven models to derive parameters for further
modeling (e.g., biophysically realistic recurrent neural network models, algorithmic
reinforcement learning models, Bayesian models) with increased efficiency and reliability
(Huys et al., 2016). Hence, combining these approaches and linking between the levels
of abstraction have the potential to increase translational benefits by relating symptoms
and cognitive functions to clinically traceable entities such as cellular processes.
For instance, AI models based on recurrent neural networks are approaching human-level
performance in many domains. Thus, if implemented with plausible biological details,
recurrent neural networks can be “meso-level” models trained to simulate patients'
clinical symptoms while the model parameters are simultaneously fitted to patients'
neuroimaging and biological data. These models often contain tens of thousands of
artificial neurons, making them large enough to model complex symptomology while remaining
tractable to study neural mechanisms in unprecedented detail. Previous work summarized
the application of such models to complex psychiatric disorders where sufficient information
about the relevant circuits exist, such as in schizophrenia (Huys et al., 2016). We
argue that there is potential to extend these applications to study underlying mechanisms
of different forms of dementia. Additionally, by integrating computational models
with neuroimaging, neuronal dysfunction underlying psychosis symptoms in dementia
such as hallucinations, delusions, paranoia could be explained by impairments in multiple
neurotransmitter systems (Marreiros et al., 2013). This could link clinical symptoms
with biological details more comprehensively than was previously available.
Discussion
Computational psychiatry and neurology endeavor a biopsychological and mechanistic
perspective by showing how each level of abstraction ranging from molecular to circuit
levels can provide a context for the human brain's hierarchical architecture, functioning
and disorders. Computational approaches provide a whole new lexicon for understanding
neural processes (Montague et al., 2012). Machine learning techniques can detect complex
and subtle mental and brain dysfunctions and their neurobiological underpinnings that
are otherwise difficult to uncover.
Computational approaches are a valuable tool moving forward in research, but the current
implementation introduces several challenges. First, the availability of good quality
data is crucial to create reliable, accurate, and robust data-driven and theory-driven
models of mental health illnesses and brain disorders. This includes the need for
widely generalisable, open access and reproducible data of different dementia types
with large sample sizes (Pellegrini et al., 2018). Second, the differences in the
standardization in the dementia care pathway across clinical practices, assessment
centers and research might pose a further challenge in appropriate data digitalisation,
curation, and integration (Wong-Lin et al., 2020). Third, researchers should additionally
ensure that data pre-processing steps do not introduce unrealistic attributes to general
healthcare datasets when used in modeling less-common types of dementia.
When interpreting models, researchers need to be conscious of potential challenges.
For instance, predictive models in psychiatry still suffer from overfitting and lack
of generalisability and validation (Meehan et al., 2022). Finding a model with the
appropriate complexity, therefore, requires finding a suitable balance between bias
and variance (Lever et al., 2016). Yet, testing and falsifying current models and
subsequent ability to develop accurate predictions of both common and rare types of
dementia are still challenging due to the limited knowledge of their neurobiology
and neuropathology.
Traditional AI modeling techniques and biophysical models often consider the macro-level
constraints on the brain and mind for very specific cognitive phenomena. However,
existing neural models do not satisfactorily provide an architectural-level explanation
for how symptoms experienced by patients were mechanistically produced by genetic,
molecular, and circuit abnormalities in different inter-connected brain regions. Moving
forward, we need models that do not only describe data but can also manipulate data
while preserving the integrity of the data. In other words, we argue to “treat the
models as if they are data”.
If present limitations are considered thoroughly, computational psychiatry and neurology
models for dementia have the potential to establish as experimental medicine platform
for personalized medicine and development of novel treatments and to advance the predictive
health systems that would support clinicians in their decision-making process (Miotto
et al., 2017). Importantly, current challenges are likely to be minimized by fast-evolving
computational fields such as AI models based on deep-learning, which can serve as
the basis of “virtual” patients. This allows for testing mechanistic hypotheses, while
maintaining the informative value obtained from real life data.
Author Contributions
LK and LS wrote the manuscript. SD and LJ contributed to the writing and provided
feedback for the manuscript. LS secured the funding and oversaw the study. All authors
have made a substantial contribution to this work and approved it for publication.
Funding
LK was the recipient of the University of Sheffield Flagship Scholarship. LS's participation
was funded by Alzheimer's Research UK Senior Research Fellowship (ARUK-SRF2017B-1).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.
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