- Email: [email protected]
Noisy Networks and Autism
Commentary
Biological Psychiatry
Noisy Networks and Autism Georg Winterer Autism spectrum disorder (ASD) is a developmental disorder characterized by a range of behavioral impairments, including deficits in social interaction and language, stereotypical repetitive patterns of behavior, and restricted interests and activities. There is an ongoing debate concerning a possible relationship of ASD to another developmental disorder with similar clinical features: schizophrenia illness. Various neurological features have been associated with ASD, such as abnormal structural and functional brain connectivity—findings that have also been reported for schizophrenia (1). During the past few years, considerable efforts have been made to provide a heuristic framework for ASD (i.e., to postulate a single mechanism that accounts for the various abnormalities on the multiple levels of observation). One recent heuristic proposal is based on the notion that ASD is characterized by high levels of endogenous neural noise, rendering neural signals “noisy” and unreliable. This suggestion is interesting, because a similar heuristic proposal was put forward for schizophrenia in a strikingly similar way by my group based on a series of experimental and computational findings (2–4) that were confirmed by others [most recently Peterson et al. (5)]. The hypothesis of noisy networks in ASD subjects was first suggested by Rubenstein and Merzenich (6) and was experimentally tested and confirmed during subsequent years by several research groups who mainly adopted electrophysiological (but also neuroimaging) approaches. In addition, there is increasing evidence that increased neural noise is related to the frequently reported abnormal functional connectivity in ASD subjects (7)—a suggestion we have made for schizophrenia as well (8). Because abnormal functional connectivity between brain regions may partly result from structural alterations of connectivities, the question arises whether the repeatedly reported abnormal structural brain connectivity in ASD toddlers is causally linked to their noisy networks. Alternatively, the causal relationship could also be the other way around, as discussed by us with regard to brain development in schizophrenia (1). In this issue of Biological Psychiatry, Lewis et al. (9) focus on the emergence of structural network inefficiencies in infants with ASD before symptom consolidation. Different from earlier studies, the major strength of their elegant longitudinal study design is that they collected data from infants who were a few months old and who were known to be at increased familial risk for ASD, and compared them with infants without an increased risk for ASD. Studies of this kind are extremely helpful because they potentially allow for the identification of early pathology underlying the cascade of events, which ultimately leads to the fully developed ASD phenotype— with regard to both neurological and behavioral changes. Using magnetic resonance imaging, the authors obtained probabilistic, seed-based tractography measures of length and
strength of connections between brain regions to assess the efficiency of information transfer for each brain region to all other brain regions, and within local subnetworks. At the same time, they assessed the relation between efficiency and symptom severity over time (at 6, 12, and 24 months of age) to ascertain the developmental progression. In essence, the following major results were obtained. In infants at risk of ASD, lower (local) network efficiency (short-range corticocortical connectivity) is already present at the age of 6 months in circumscribed brain regions that are associated with low-level sensory processing, such as the primary and secondary auditory cortices. As the infants get older, lower network efficiency spreads to brain regions involved in higher-level processing (e.g., related to language [Broca’s area]), areas that require the integration of information from more distant brain areas (long-range corticocortical connectivity). The (local) reduction in efficiencies is present before the onset of clinical symptoms, while there is a relationship between efficiency and 24-month symptom severity. The authors argue that their findings are consistent with the conjecture that some of the most developmentally proximal deficits in ASD are in low-level sensory processing and that the abnormalities in higher-level processes stem from these low-level inefficiencies. In other words, abnormalities in higher-level processes are regarded as secondary cascade effects resulting from abnormalities in lowlevel processes. Theoretically, one might bring up the objection that the sequence of events in time is not necessarily causal. The same neuropathology simply may manifest itself at a later point during neurodevelopment in brain regions that are associated with higher-level processing, which the authors admit. However, Lewis et al. make a strong point when arguing that neural abnormalities that yield basic deficits early in development are likely to have cascade effects over developmental time through interaction with the brain. In any case, what is interesting about the “cascade suggestion” is that once again there might be a similarity to schizophrenia development; several longitudinal magnetic resonance imaging studies of subjects at risk for schizophrenia also suggest more localized structural abnormalities of the brain at the prodromal stages of the illness (e.g., in the temporal lobe), which then spread throughout the brain. Ultimately, and referring to research done by other groups, Lewis et al. prefer the notion that children with ASD may show diminished developmental synaptic pruning that in turn may impact on the efficiency of information transfer. Even so, the authors again admit that a variety of other molecular mechanisms should also be taken into account. The latter limitation is of course a problem inherent to all neuropsychiatric disorders, such as schizophrenia (i.e., it is highly unlikely that one single molecular mechanisms explains the bulk of the phenotype).
SEE CORRESPONDING ARTICLE ON PAGE 176
152 & 2017 Society of Biological Psychiatry. Biological Psychiatry August 1, 2017; 82:152–154 www.sobp.org/journal
http://dx.doi.org/10.1016/j.biopsych.2017.05.011 ISSN: 0006-3223
Biological Psychiatry
Commentary
It is certainly undisputable that ASD and schizophrenia are different disease entities on the phenotypic level. Perhaps the most important difference is that the illnesses typically emerge at different ages. By extension, this also suggests that different molecular mechanisms play a role. On the other hand, it also obvious that certain similarities exist on the clinical level—in particular when the comparison is made with childhood-onset schizophrenia. Social withdrawal, impaired communication, and poor eye contact are seen as part of the negative symptom complex in ASD and schizophrenia patients. In higher-functioning individuals with ASD, schizophrenia-positive symptoms, such as thought disorder and paranoia, are also reported. As it appears now, there are also shared properties on the (macroscopic) systems level as assessed with neuroimaging and electrophysiology (see above). Accordingly, it is conceivable that ASD and schizophrenia may share at least some of the molecular anomalies. When trying to track phenotypic changes of an illness with the aim to identify underlying molecular abnormalities, it is most likely helpful to use phenotypic markers that are as closely related to the molecular pathways of interest as possible. With regard to ASD, tracking structural network efficiency may turn out to be the gold standard in the foreseeable future. However, both structure and function should be taken into account to obtain a full picture. In fact, it is not yet clear what is observed first: abnormal structure or function. In addition, a true understanding of an illness is generally only possible when the precise relationship between abnormal structure and function is established and how this impacts abnormal behavior. Based on our computational model of schizophrenia (4), the following suggestion is made for ASD to integrate abnormal behavior, brain structure, and function. This suggestion can be regarded as an extension of the heuristic proposal that ASD is characterized by high levels of endogenous neural noise. This proposal is based on our concept of attractor dynamics and noise introduced by the spiking dynamics of neurons in schizophrenia (Figure 1). When applying this “concept of increased noise” to the “cascade concept” of Lewis et al. (9), the following scenario is conceivable. Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity, which translates correlated activity patterns into changes in synaptic strength (10). With regard to ASD (and perhaps schizophrenia), one could image that a more or less localized inefficient network structure is a source of additional (too much) noise at the starting point. This source of noise interferes with local correlation of spiking activity, which eventually—by interaction—introduces noise into more distant neural networks impairing structural refinement of those networks as well and so on—no less than a dramatic cascade of noise throughout the brain. This in turn would explain the finding of Lewis et al. (9) that structural network inefficiencies become more and more widespread in ASD toddlers with increasing age, which ultimately may lead to their clinical symptoms. Of course, we do not know whether this notion with a mutual interaction of noise versus refinement of structure during neurodevelopment holds true in ASD—and by extension, in schizophrenia. However, it is a testable hypothesis, in both ASD and schizophrenia. What is required
Figure 1. (Top panel) Attractor network architecture of the cerebral cortex (usually implemented by integrate-and-fire neurons and realistic synaptic dynamics). The stability of an attractor (internal representation of a mental state) is characterized by the average time in which the system stays in the basin of attraction (potential) under the influence of stochastic noise. Stochastic noise is a property of all biological networks to guarantee proper functioning equal to stochastic resonance (e.g., low heart rate variability is associated with a higher risk of death in patients with heart disease). The presence of noise provokes transitions between attractor states. However, when increasing noise in the system beyond a certain level, transitions between attractor states occur more frequently and stable attractors can no longer be formed. (Bottom panel) In both the left scenario (negative symptoms in autism spectrum disorder/schizophrenia) and the right scenario (positive symptoms in autism spectrum disorder/schizophrenia), there is too much noise in the system (reduced signal-to-noise ratio). The negative symptoms (left) are attributed to a reduction in the depths of the basins of attraction of the high firing rate (persistent) attractor states. This results in a poor internal representation of items with diminished cognitive performance, including restricted activity and communication. The positive symptoms (right) are related a flat energy landscape in which both the spontaneous (low firing rate) and the persistent (high firing rate) are shallow. In this scenario, the same type of stochastic noise can make the network jump from a spontaneous state of firing (in which there is no internal representation of an item [e.g., a short-term memory]) to an active state in which one of the items (e.g., short-term memories) become arbitrarily active, which might represent an intrusive thought or a hallucination. [Adapted from Rolls et al. (4).]
is a similar clinical study design as that used by Lewis et al. The only additional requirement is the collection of functional neuroimaging data, ideally combined with the collection of electrophysiological data—the latter having the advantage that a large electrophysiological normative reference database exists for children in numerous pediatric neurology departments.
Acknowledgments and Disclosures The author reports no biomedical financial interests or potential conflicts of interest.
Article Information From the Experimental and Clinical Research Center, Charité–University Medicine Berlin, Berlin, Germany.
Biological Psychiatry August 1, 2017; 82:152–154 www.sobp.org/journal
153
Biological Psychiatry
Commentary
Address correspondence to Georg Winterer, M.D., Ph.D, Experimental and Clinical Research Center, Charité–Universitätsmedizin Berlin, Campus Berlin-Buch, Lindenberger Weg 80, 13125 Berlin, Germany; Email: [email protected]. Received May 11, 2017; accepted May 11, 2017.
4.
5.
6.
References 1.
2.
3.
154
Konrad A, Winterer G (2008): Disturbed structural connectivity in schizophrenia primary factor in pathology or epiphenomenon? Schizophr Bull 34:72–92. Winterer G, Ziller M, Dorn H, Frick K, Mulert C, Wuebben Y, et al. (2000): Schizophrenia: Reduced signal-to-noise ratio and impaired phase-locking during information processing. Clin Neurophysiol 111: 837–849. Winterer G, Coppola R, Goldberg TE, Egan MF, Jones DW, Sanchez CE, Weinberger DR (2004): Prefrontal broadband noise, working memory, and genetic risk for schizophrenia. Am J Psychiatry 161: 490–500.
7.
8.
9.
10.
Rolls ET, Loh M, Deco G, Winterer G (2008): Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nat Rev Neurosci 9:696–709. Peterson EJ, Rosen BQ, Campbell AM, Belger A, Voytek B (2017): 1/f neural noise is a better predictor of schizophrenia than neural oscillations [published online ahead of print Mar 8]. bioRxiv. Rubenstein JL, Merzenich MM (2003): Model of autism: Increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav 2:255–267. Domínguez LG, Velázquez JL, Galán RF (2013): A model of functional brain activity and background noise as a biomarker for cognitive phenotypes: Application to autism. PLoS One 8:e61493. Winterer G, Coppola R, Egan MF, Goldberg TE, Weinberger DR (2003): Functional and effective frontotemporal connectivity and genetic risk for schizophrenia. Biol Psychiatry 54:1181–1192. Lewis JD, Evans AC, Pruett JR, Botteron KN, McKinstry RC, Zwaigenbaum L, et al. (2017): The emergence of network inefficiencies in infants with autism spectrum disorder. Biol Psychiatry 82:176–185. Bennett JE, Bair W (2016): Refinement and pattern formation in neural circuits by the interaction of traveling waves with spike-timing dependent plasticity. PLoS Comput Biol 11:e1004422.
Biological Psychiatry August 1, 2017; 82:152–154 www.sobp.org/journal
Biological Psychiatry
Noisy Networks and Autism Georg Winterer Autism spectrum disorder (ASD) is a developmental disorder characterized by a range of behavioral impairments, including deficits in social interaction and language, stereotypical repetitive patterns of behavior, and restricted interests and activities. There is an ongoing debate concerning a possible relationship of ASD to another developmental disorder with similar clinical features: schizophrenia illness. Various neurological features have been associated with ASD, such as abnormal structural and functional brain connectivity—findings that have also been reported for schizophrenia (1). During the past few years, considerable efforts have been made to provide a heuristic framework for ASD (i.e., to postulate a single mechanism that accounts for the various abnormalities on the multiple levels of observation). One recent heuristic proposal is based on the notion that ASD is characterized by high levels of endogenous neural noise, rendering neural signals “noisy” and unreliable. This suggestion is interesting, because a similar heuristic proposal was put forward for schizophrenia in a strikingly similar way by my group based on a series of experimental and computational findings (2–4) that were confirmed by others [most recently Peterson et al. (5)]. The hypothesis of noisy networks in ASD subjects was first suggested by Rubenstein and Merzenich (6) and was experimentally tested and confirmed during subsequent years by several research groups who mainly adopted electrophysiological (but also neuroimaging) approaches. In addition, there is increasing evidence that increased neural noise is related to the frequently reported abnormal functional connectivity in ASD subjects (7)—a suggestion we have made for schizophrenia as well (8). Because abnormal functional connectivity between brain regions may partly result from structural alterations of connectivities, the question arises whether the repeatedly reported abnormal structural brain connectivity in ASD toddlers is causally linked to their noisy networks. Alternatively, the causal relationship could also be the other way around, as discussed by us with regard to brain development in schizophrenia (1). In this issue of Biological Psychiatry, Lewis et al. (9) focus on the emergence of structural network inefficiencies in infants with ASD before symptom consolidation. Different from earlier studies, the major strength of their elegant longitudinal study design is that they collected data from infants who were a few months old and who were known to be at increased familial risk for ASD, and compared them with infants without an increased risk for ASD. Studies of this kind are extremely helpful because they potentially allow for the identification of early pathology underlying the cascade of events, which ultimately leads to the fully developed ASD phenotype— with regard to both neurological and behavioral changes. Using magnetic resonance imaging, the authors obtained probabilistic, seed-based tractography measures of length and
strength of connections between brain regions to assess the efficiency of information transfer for each brain region to all other brain regions, and within local subnetworks. At the same time, they assessed the relation between efficiency and symptom severity over time (at 6, 12, and 24 months of age) to ascertain the developmental progression. In essence, the following major results were obtained. In infants at risk of ASD, lower (local) network efficiency (short-range corticocortical connectivity) is already present at the age of 6 months in circumscribed brain regions that are associated with low-level sensory processing, such as the primary and secondary auditory cortices. As the infants get older, lower network efficiency spreads to brain regions involved in higher-level processing (e.g., related to language [Broca’s area]), areas that require the integration of information from more distant brain areas (long-range corticocortical connectivity). The (local) reduction in efficiencies is present before the onset of clinical symptoms, while there is a relationship between efficiency and 24-month symptom severity. The authors argue that their findings are consistent with the conjecture that some of the most developmentally proximal deficits in ASD are in low-level sensory processing and that the abnormalities in higher-level processes stem from these low-level inefficiencies. In other words, abnormalities in higher-level processes are regarded as secondary cascade effects resulting from abnormalities in lowlevel processes. Theoretically, one might bring up the objection that the sequence of events in time is not necessarily causal. The same neuropathology simply may manifest itself at a later point during neurodevelopment in brain regions that are associated with higher-level processing, which the authors admit. However, Lewis et al. make a strong point when arguing that neural abnormalities that yield basic deficits early in development are likely to have cascade effects over developmental time through interaction with the brain. In any case, what is interesting about the “cascade suggestion” is that once again there might be a similarity to schizophrenia development; several longitudinal magnetic resonance imaging studies of subjects at risk for schizophrenia also suggest more localized structural abnormalities of the brain at the prodromal stages of the illness (e.g., in the temporal lobe), which then spread throughout the brain. Ultimately, and referring to research done by other groups, Lewis et al. prefer the notion that children with ASD may show diminished developmental synaptic pruning that in turn may impact on the efficiency of information transfer. Even so, the authors again admit that a variety of other molecular mechanisms should also be taken into account. The latter limitation is of course a problem inherent to all neuropsychiatric disorders, such as schizophrenia (i.e., it is highly unlikely that one single molecular mechanisms explains the bulk of the phenotype).
SEE CORRESPONDING ARTICLE ON PAGE 176
152 & 2017 Society of Biological Psychiatry. Biological Psychiatry August 1, 2017; 82:152–154 www.sobp.org/journal
http://dx.doi.org/10.1016/j.biopsych.2017.05.011 ISSN: 0006-3223
Biological Psychiatry
Commentary
It is certainly undisputable that ASD and schizophrenia are different disease entities on the phenotypic level. Perhaps the most important difference is that the illnesses typically emerge at different ages. By extension, this also suggests that different molecular mechanisms play a role. On the other hand, it also obvious that certain similarities exist on the clinical level—in particular when the comparison is made with childhood-onset schizophrenia. Social withdrawal, impaired communication, and poor eye contact are seen as part of the negative symptom complex in ASD and schizophrenia patients. In higher-functioning individuals with ASD, schizophrenia-positive symptoms, such as thought disorder and paranoia, are also reported. As it appears now, there are also shared properties on the (macroscopic) systems level as assessed with neuroimaging and electrophysiology (see above). Accordingly, it is conceivable that ASD and schizophrenia may share at least some of the molecular anomalies. When trying to track phenotypic changes of an illness with the aim to identify underlying molecular abnormalities, it is most likely helpful to use phenotypic markers that are as closely related to the molecular pathways of interest as possible. With regard to ASD, tracking structural network efficiency may turn out to be the gold standard in the foreseeable future. However, both structure and function should be taken into account to obtain a full picture. In fact, it is not yet clear what is observed first: abnormal structure or function. In addition, a true understanding of an illness is generally only possible when the precise relationship between abnormal structure and function is established and how this impacts abnormal behavior. Based on our computational model of schizophrenia (4), the following suggestion is made for ASD to integrate abnormal behavior, brain structure, and function. This suggestion can be regarded as an extension of the heuristic proposal that ASD is characterized by high levels of endogenous neural noise. This proposal is based on our concept of attractor dynamics and noise introduced by the spiking dynamics of neurons in schizophrenia (Figure 1). When applying this “concept of increased noise” to the “cascade concept” of Lewis et al. (9), the following scenario is conceivable. Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity, which translates correlated activity patterns into changes in synaptic strength (10). With regard to ASD (and perhaps schizophrenia), one could image that a more or less localized inefficient network structure is a source of additional (too much) noise at the starting point. This source of noise interferes with local correlation of spiking activity, which eventually—by interaction—introduces noise into more distant neural networks impairing structural refinement of those networks as well and so on—no less than a dramatic cascade of noise throughout the brain. This in turn would explain the finding of Lewis et al. (9) that structural network inefficiencies become more and more widespread in ASD toddlers with increasing age, which ultimately may lead to their clinical symptoms. Of course, we do not know whether this notion with a mutual interaction of noise versus refinement of structure during neurodevelopment holds true in ASD—and by extension, in schizophrenia. However, it is a testable hypothesis, in both ASD and schizophrenia. What is required
Figure 1. (Top panel) Attractor network architecture of the cerebral cortex (usually implemented by integrate-and-fire neurons and realistic synaptic dynamics). The stability of an attractor (internal representation of a mental state) is characterized by the average time in which the system stays in the basin of attraction (potential) under the influence of stochastic noise. Stochastic noise is a property of all biological networks to guarantee proper functioning equal to stochastic resonance (e.g., low heart rate variability is associated with a higher risk of death in patients with heart disease). The presence of noise provokes transitions between attractor states. However, when increasing noise in the system beyond a certain level, transitions between attractor states occur more frequently and stable attractors can no longer be formed. (Bottom panel) In both the left scenario (negative symptoms in autism spectrum disorder/schizophrenia) and the right scenario (positive symptoms in autism spectrum disorder/schizophrenia), there is too much noise in the system (reduced signal-to-noise ratio). The negative symptoms (left) are attributed to a reduction in the depths of the basins of attraction of the high firing rate (persistent) attractor states. This results in a poor internal representation of items with diminished cognitive performance, including restricted activity and communication. The positive symptoms (right) are related a flat energy landscape in which both the spontaneous (low firing rate) and the persistent (high firing rate) are shallow. In this scenario, the same type of stochastic noise can make the network jump from a spontaneous state of firing (in which there is no internal representation of an item [e.g., a short-term memory]) to an active state in which one of the items (e.g., short-term memories) become arbitrarily active, which might represent an intrusive thought or a hallucination. [Adapted from Rolls et al. (4).]
is a similar clinical study design as that used by Lewis et al. The only additional requirement is the collection of functional neuroimaging data, ideally combined with the collection of electrophysiological data—the latter having the advantage that a large electrophysiological normative reference database exists for children in numerous pediatric neurology departments.
Acknowledgments and Disclosures The author reports no biomedical financial interests or potential conflicts of interest.
Article Information From the Experimental and Clinical Research Center, Charité–University Medicine Berlin, Berlin, Germany.
Biological Psychiatry August 1, 2017; 82:152–154 www.sobp.org/journal
153
Biological Psychiatry
Commentary
Address correspondence to Georg Winterer, M.D., Ph.D, Experimental and Clinical Research Center, Charité–Universitätsmedizin Berlin, Campus Berlin-Buch, Lindenberger Weg 80, 13125 Berlin, Germany; Email: [email protected]. Received May 11, 2017; accepted May 11, 2017.
4.
5.
6.
References 1.
2.
3.
154
Konrad A, Winterer G (2008): Disturbed structural connectivity in schizophrenia primary factor in pathology or epiphenomenon? Schizophr Bull 34:72–92. Winterer G, Ziller M, Dorn H, Frick K, Mulert C, Wuebben Y, et al. (2000): Schizophrenia: Reduced signal-to-noise ratio and impaired phase-locking during information processing. Clin Neurophysiol 111: 837–849. Winterer G, Coppola R, Goldberg TE, Egan MF, Jones DW, Sanchez CE, Weinberger DR (2004): Prefrontal broadband noise, working memory, and genetic risk for schizophrenia. Am J Psychiatry 161: 490–500.
7.
8.
9.
10.
Rolls ET, Loh M, Deco G, Winterer G (2008): Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nat Rev Neurosci 9:696–709. Peterson EJ, Rosen BQ, Campbell AM, Belger A, Voytek B (2017): 1/f neural noise is a better predictor of schizophrenia than neural oscillations [published online ahead of print Mar 8]. bioRxiv. Rubenstein JL, Merzenich MM (2003): Model of autism: Increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav 2:255–267. Domínguez LG, Velázquez JL, Galán RF (2013): A model of functional brain activity and background noise as a biomarker for cognitive phenotypes: Application to autism. PLoS One 8:e61493. Winterer G, Coppola R, Egan MF, Goldberg TE, Weinberger DR (2003): Functional and effective frontotemporal connectivity and genetic risk for schizophrenia. Biol Psychiatry 54:1181–1192. Lewis JD, Evans AC, Pruett JR, Botteron KN, McKinstry RC, Zwaigenbaum L, et al. (2017): The emergence of network inefficiencies in infants with autism spectrum disorder. Biol Psychiatry 82:176–185. Bennett JE, Bair W (2016): Refinement and pattern formation in neural circuits by the interaction of traveling waves with spike-timing dependent plasticity. PLoS Comput Biol 11:e1004422.
Biological Psychiatry August 1, 2017; 82:152–154 www.sobp.org/journal