By mimicking information processing and cognition in the human and primate brain (Kriegeskorte, 2015; Yamins and DiCarlo, 2016; Richards et al., 2019), deep neural networks have been shown to be capable of outperforming conventional machine learning methods for various classification and regression tasks, such as visual object recognition (LeCun et al., 2015; Vercio et al., 2020). Cognitive neuroscience continues to influence and advance the development of more biologically informed deep neural networks to further improve classification performance and achieve more human-like results (Kriegeskorte and Douglas, 2018). However, the potential for using deep neural networks as computational models of the human brain to advance our understanding of neurological diseases, such as Alzheimer’s disease, has been largely neglected (Khatami et al., 2020).

In this work, we propose a paradigm using deep neural networks as in silico models of neurodegeneration (Figure 1A). Within this context, in silico refers to employing computer models to improve our understanding of normal and pathological processes in the living organism. To date, in the context of using deep neural networks as models of normal cognitive function, this has been mostly studied using deep convolutional neural networks (DCNNs) with a focus on visual cognition (Cadieu et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Yamins et al., 2014; Hong et al., 2016; Kheradpisheh et al., 2016; Horikawa and Kamitani, 2017; Wen et al., 2018; Grossman et al., 2019; Horikawa et al., 2019). The connectivity and hierarchical organization of DCNNs are inspired by the mammalian visual cortex, which make them promising biomimetic models for this purpose (Kriegeskorte, 2015; Yamins and DiCarlo, 2016; Peters and Kriegeskorte, 2021). DCNNs are state-of-the-art models for the prediction of neural responses of the ventral visual stream, particularly in higher order regions such as visual area V4 and inferior temporal cortex, during visual object recognition (Cadieu et al., 2014; Yamins et al., 2014). Additionally, it has been shown that the internal representation of objects (Cadieu et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014) and faces (Grossman et al., 2019) in DCNNs are similar to the internal representations in humans. Consequently, current efforts aim to quantify the similarity of different DCNN architectures to the human brain through integrative benchmarking, such as the Brain-Score (Schrimpf et al., 2020).

Prior work in modeling neurological diseases have primarily explored computational models of cellular processes focusing on pathological changes such as excitotoxicity or abnormal bioenergetics (Le Masson et al., 2014; Muddapu et al., 2019), or connectome models based on structural or functional connectivity in the brain (Hof et al., 1997; Raj et al., 2012; Zhou et al., 2012; Ortiz et al., 2015; Peraza-Goicolea et al., 2020; Vanasse et al., 2021). Early work investigating structural or functional connectomes primarily focused on modeling specific aspects of disease progression, such as diffusive spread of misfolded tau and beta amyloids (Raj et al., 2012) or changes in network connectivity contributing to disease vulnerability or diagnosis (Zhou et al., 2012; Ortiz et al., 2015). More recent work has begun using the connectome to simulate disease states in silico, for example by modifying connection weights in a simulated functional connectome to predict changes in functional activation and connectivity in the brain (Peraza-Goicolea et al., 2020). A meta-analysis of the relationship between structural pathology and behavioral pathology supported the notion that network degeneration is a contributing factor to disease pathology (Vanasse et al., 2021). However, none of the aforementioned approaches used models that can perform tasks at near-human levels (LeCun et al., 2015; Vercio et al., 2020). Doing so would enable the simulation of changes in model behavior with disease progression in silico.

Deep neural network-based approaches have largely focused on data-driven analysis (Khatami et al., 2020), such as disease diagnosis or progression prediction (Lee et al., 2019; Martinez-Murcia et al., 2019). One notable exception is the work by Lusch et al. (2018) who used a DCNN to model traumatic brain injury (TBI) as focal axonal swellings that cause spike train blockage. In these analyses, TBI was simulated by ablating DCNN weights. The work by Lusch et al. (2018) focused primarily on abruptly injuring the DCNN, with a very limited investigation of cumulative network injury, and did not quantify changes in artificial neuron activation patterns or representations. Additionally, the similarity of the DCNNs used in the study to biological neural networks, and thus their validity as brain-like models of visual cognition, were not investigated (Schrimpf et al., 2020).

In this study, we used DCNNs trained for visual object recognition to model posterior cortical atrophy (PCA), an atypical form of Alzheimer’s marked by lesions in the visual cortex and attendant visual cognition deficits such as visual agnosia (Tang-Wai et al., 2004; Crutch et al., 2012; Migliaccio et al., 2016; Silva et al., 2017). Specifically, we trained the VGG-19 DCNN (Simonyan and Zisserman, 2014) to perform visual object recognition on the CIFAR-100 dataset (Krizhevsky, 2009). VGG-19 has one of the highest similarities to biological neural networks amongst feed-forward DCNNs as measured by the Brain-Score (Schrimpf et al., 2020). Trained networks underwent neurodegeneration, where connections between artificial neurons, mimicking biological synapses, were progressively lesioned. At each stage of simulated PCA we evaluated the respective changes in the injured network’s object classification performance and activation patterns of its neurons.

Object recognition performance was already impaired by simulated PCA injury of just 0.2% of connections (Figure 2A). By 30% injury, the network performance was reduced to chance level (1%). RDMs, used to analyze object representations within degenerating networks, showed that representations became more homogenous with injury (Figure 2B). Though object-level representations deteriorated markedly with injury, the networks retained some structured internal representations (Figure 2C). Even when networks lost all object recognition capability after 30% injury, the deteriorated internal representations retained correlation with the representations of uninjured networks and remained above the level of random noise (0.005%).

Next, we investigated whether simulated PCA impacted the networks’ representational capacity at all levels of semantic hierarchy equally by analyzing superclass-level RDMs. The corresponding results showed that trained networks were generally able to infer the existence of the categorical superclasses in the CIFAR-100 dataset, despite never have been given this information explicitly (Figure 3A). The internal representations of categorical superclass and broader image categories, such as animate vs. inanimate or natural vs. artificial, were better retained with injury compared to object-level representations (Figures 3A,B). This increased stability of superclass representation was also reflected in the manner in which networks made mistakes at the initial stages of simulated PCA, as objects were misclassified for another object within the same superclass (e.g., a rose is misclassified as another type of flower) at the same rate as uninjured networks until 1.3% injury; thereafter, the networks made more random errors (Figure 3C).

Deep learning is a powerful biomimetic paradigm for modeling cognitive processes and information processing in the brain (Richards et al., 2019). Efforts thus far on using deep learning to study the human brain have primarily focused on modeling normal function (Cadieu et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Yamins et al., 2014; Hong et al., 2016; Kheradpisheh et al., 2016; Horikawa and Kamitani, 2017; Kell et al., 2018; Pandarinath et al., 2018; Wen et al., 2018; Grossman et al., 2019; Horikawa et al., 2019; Botvinick et al., 2020; Hashemzadeh et al., 2020; Caucheteux et al., 2021). In this paper, we present an important new direction, namely using deep learning for modeling brain dysfunction in silico. As deep neural networks incorporate neurally mechanistic features to increase their biological plausibility, it will be important to investigate how well they model both normal function and disease if they are to become truly brain-like (Kriegeskorte, 2015; Yamins and DiCarlo, 2016; Kriegeskorte and Douglas, 2018; Richards et al., 2019).

Based on the simulated PCA in a DCNN, it was shown that increasing functional deficits were associated with greater levels of injury in our artificial neural networks, parallel to increased visual agnosia with greater atrophy (Hof et al., 1997; Fox et al., 1999; Zarei et al., 2013; Silva et al., 2017). At the initial stages of simulated PCA, the injured networks tended to misclassify objects as another similar item, akin to misidentifying a fork as a knife by patients with visual agnosia (Milner et al., 1991). Simulated neurodegeneration affected object-level representations more severely than broader categorical-representations, similar to the loss of object-specific knowledge occurring prior to object category knowledge loss in patients with semantic dementia (Hodges et al., 1995).

This study is a first step in investigating the use of deep neural networks as in silico models of neural injury. As such, limitations on the current work and avenues for future investigations must be highlighted. Notably, the in silico model was more sensitive to injury compared to the human brain, showing measurable impairments after just 0.2% injury. In contrast, cognitive impairments do not typically manifest until much greater levels of cortical atrophy (Fox et al., 1999; Zarei et al., 2013). However, this must also take into consideration that clinical detection of PCA often occurs late in the disease course. PCA is easily misdiagnosed, with visual impairment interpreted as ophthalmologic or refractive problems, and cortical dysfunction considered only when cognitive impairment is more overt, resulting in accurate assessment and treatment (Crutch et al., 2012). Although a disease time course for PCA is not available, Lehmann et al. (2011) reported a 10–20% decrease of cortical thickness in the occipitoparietal and occipitotemporal regions of patients diagnosed with PCA (Lehmann et al., 2011), with gray matter loss of approximately 2% of whole brain volume per year (Lehmann et al., 2012). PCA patients show a range of cognitive deficits, with one study reporting a 40–50% decrease in visual task performance compared to controls (McMonagle et al., 2006). This magnitude of deficit is comparable with the decrease in performance with 10–20% injury in our model, though more rigorous comparison between deficits in clinical and simulated PCA are needed.

Another limitation is that our simulation assumed a static system that does not change its connectivity over the course of PCA. In contrast, the brain is a dynamic system that undergoes synaptic plasticity and functional connectivity changes in response to the insult itself, as well as to rehabilitative and pharmacologic interventions (Voss et al., 2017). This plasticity could be added to our in silico model by retraining the damaged DCNNs in between inflicting injuries. While this is outside the scope of this preliminary study, research efforts on DCNN pruning may provide some insight on injured network retraining. Pruning is often undertaken to compress networks by selectively removing weights to reduce the size and computational demand of the networks, for example by removing low magnitude weights (Han et al., 2015). However, with sufficient pruning, the networks will eventually suffer large declines in performance. To mitigate this, the networks can be retrained, such as after pruning or over the course of progressive pruning (Mittal et al., 2019; Marcin and Maciej, 2020). It has been shown that this retraining allows the removal of a substantially larger number of connections while retaining comparable performance. Applying these retraining algorithms to the injured networks may provide a way to mimic neuroplasticity in silico, which will be essential to capturing the complexities of neural injury in more biologically plausible models.

Moreover, the current study used weight ablation to simulate progressive neurodegeneration in a DCNN. However, it remains to be seen if alternative injury types may be more appropriate for simulating PCA or other neurodegenerative diseases. The network weight ablation injury used here simulates synaptic injury, whereas cortical atrophy seen in PCA affects both neurons and synapses (Lehmann et al., 2011, 2012). Thus, alternative approaches to modeling could include network node ablation to simulate neuronal injury, or a combination of weight and network node ablation to simulate a combination of synaptic and neuronal injury. Furthermore, alternative network modifications, such as randomly rewiring network nodes or setting network weights to random values instead of zero (Gaier and Ha, 2019; Xie et al., 2019), may also be studied to determine their appropriateness for modeling PCA or other types of dysfunction seen in neurodegenerative and neurological diseases.

Alternative courses of simulating the progressive injury seen in PCA and other neurodegenerative diseases may also be explored. For example, the current study used a sudden ablation injury of randomly selected weights across the entire network, with progressive injury simulated by increasing the number of ablated weights. An alternative approach could be to progressively decrease injured synapses to zero or add increasing noise to the weights. However, it remains to be seen whether this better recapitulates the slower manifestation of cognitive deficits seen clinically (Fox et al., 1999; Zarei et al., 2013). In contrast to random weight ablation, ablation of connections based on their strengths such as in network pruning (Han et al., 2015; Mittal et al., 2019; Marcin and Maciej, 2020), or specifically targeting excitatory (positive) or inhibitory (negative) connections (Song et al., 2016; Mackwood et al., 2021) may be instructive.

Future work may also investigate more biologically informed methods for modeling disease. One possibility may be to model the diffusive spread of disease-causing agents such as misfolded tau and beta amyloids across artificial neurons and synapses in the deep neural network (Raj et al., 2012; Vogel et al., 2021). Another approach may be to injure individual DCNN layers based on their correspondence to regions of the ventral visual stream such as V4 and inferior temporal cortex (Cadieu et al., 2014; Yamins et al., 2014). This layer-wise injury could be informed by neuroimaging, such as patterns of atrophy in PCA (Whitwell et al., 2007; Lehmann et al., 2011, 2012).

Finally, this paradigm of simulating neural injury can be extended to deep neural networks in other cognitive domains such as motor control (Pandarinath et al., 2018), auditory cognition (Kell et al., 2018), language processing (Hashemzadeh et al., 2020; Caucheteux et al., 2021), memory (Schapiro et al., 2017), and decision making (Botvinick et al., 2020). This may enable the development of in silico models of other neurological diseases, such as Parkinson’s disease, and the study of their impact on multiple cognitive systems.

In conclusion, our results show that deep neural networks may serve as in silico models of neurodegeneration, as the injured network’s behavior is similar to that seen clinically. Future work will need to study this correspondence with clinical data in more detail. Marrying in silico disease modeling with clinical data may enable the creation of patient-specific computational models, and may be integral to precision medicine for neurological diseases.

The CIFAR-100 dataset analyzed for this study is publicly available at https://www.cs.toronto.edu/~kriz/cifar.html. The pretrained VGG-19 used for this study is available from Tensorflow 2.0. The source code for fine-tuning pretrained VGG-19 models on CIFAR-100, for injuring models, and for evaluating their performance and RDMs are available from the corresponding author upon request.

AT, JM, and NF designed the study. AT and JM developed the computer code. AT performed the research, data analysis and drafted the manuscript. All authors revised the manuscript and approved the submitted version.

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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