The study of patients with brain lesions has made major historical contributions to cognitive neuroscience. Here I argue for an increased investment in modern lesion mapping, complementing fMRI studies and laying the conceptual and analytic foundations for future techniques that could experimentally manipulate human brain function

Adolphs, Ralph

Caltech Authors - Main

Human Lesion Studies in the 21st Century
Ralph Adolphs1,*
1Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 
91125, USA
Abstract
The study of patients with brain lesions has made major historical contributions to cognitive 
neuroscience. Here I argue for an increased investment in modern lesion mapping, complementing 
fMRI studies and laying the conceptual and analytic foundations for future techniques that could 
experimentally manipulate human brain function.
The Challenge and Promise of Lesion Studies
Remarkable alterations in cognition and behavior in lesion patients have been widely 
reported and popularized, and are often thought to have been the prerequisite for the 
emergence of cognitive neuroscience (Geschwind, 1997; Damasio and Damasio, 1989). But 
what exactly do such studies tell us, and what place do they find amidst the vastly more 
common approach of functional neuroimaging nowadays?
These questions typically generate two very different sets of answers. One set argues for the 
indispensable value of lesion studies and the dissociations in function they provide. Lesions 
give us insight into the causally necessary function of brain structures, whereas 
electrophysiology and fMRI reflect mere correlations with psychological processes. Lesions 
show us dissociations in cognition we could never have hypothesized, and thus can radically 
change our model of the architecture of the mind. And careful characterization of the deficits 
following lesions and their change over time provides clinically valuable information not 
only about the constellation of impairments produced, but also about their compensation and 
possible resolution over time. When all these pieces are put together with modern tools, data 
from lesion patients seem to show us how dynamic brain networks depend on the function of 
particular components, how cognition can be at least partially decomposed into modules of 
sorts, and how degeneracy and plasticity come into play. In short, lesion studies continue to 
offer unique value for cognitive psychology, cognitive neuroscience, clinical 
neuropsychology, and even cognitive science and philosophy of mind more generally.
A second set of answers is less sanguine. It is instead argued that the famous lesion studies 
can be counted on the fingers of one hand, do not generalize, and were actually wrong in 
most of the details. Furthermore, modern lesion studies are no better: every patient is 
different in idiosyncratic ways, the lesions are far too coarse since they cannot be 
*Correspondence: radolphs@caltech.edu. 
HHS Public Access
Author manuscript
Neuron. Author manuscript; available in PMC 2017 October 25.
Published in final edited form as:
Neuron. 2016 June 15; 90(6): 1151–1153. doi:10.1016/j.neuron.2016.05.014.
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experimentally produced, and fMRI has already made the lesion method obsolete (see 
Rorden and Karnath, 2004 for a synopsis of these views and counterarguments).
The far more recent history of fMRI research has had its own share of ups and downs in the 
public image, but nobody concluded from debates about statistical or other technical 
difficulties that we should abandon fMRI. A huge effort has instead been put into developing 
more and more refined tools and into accumulating large shared datasets for fMRI analyses. 
Indeed, much of this development has in fact been driven by the temporary crises around 
replication and statistical power that the field has weathered. Many of the same problems 
apply to lesion studies. Happily, so do many of the same solutions. We need better statistical 
reliability and generalizability, more complex models that consider (for example) white 
matter and gray matter separately, and multivariate analyses. But these are all eminently 
surmountable problems. There is no inherent limitation here.
So one answer is that the criticisms outlined above are outdated. Yes, classical studies had 
single cases and were crude in many ways (so, too, were early neuroimaging studies), but 
these are problems that should be and can be solved, not inherent barriers to the method. For 
example, new multivariate algorithms for lesion-symptom mapping are now being applied 
(Smith et al., 2013; Mah et al., 2014). These algorithms can build in sophisticated 
information about the statistical anatomical dependencies between voxels in lesion analysis. 
In the case of a stroke, for example, the likelihood of different voxels being lesioned or not 
is highly correlated across an anatomically complex shape that results from how blood 
vessels are distributed in the brain. Multivariate algorithms can incorporate this information 
into their analysis, can treat gray and white matter separately, and can look for distributed 
patterns of lesions as the best correspondence to a cognitive process. These approaches 
address many of the methodological complaints that have been leveled against lesion 
mapping in the past. Another difficulty is that the lesion interacts with many other variables
—so we need models that incorporate these other variables (age, sex, IQ, etc.). Such more 
complex models are also straightforward to construct in principle. However, these advances 
all require one key ingredient: a very large sample size. Even hundreds of lesion patients 
won’t do it; we need more than that. But there is actually no shortage of potential data at all. 
The challenge is how to collect and coordinate it.
It is estimated that there are about 800,000 inpatients a year in the U.S. with a primary 
diagnosis of stroke (Mozaffarian et al., 2015). There are a smaller number with brain injury 
from other causes like tumors or epilepsy surgery, which offer complementary strengths and 
confounds for comparison. Although the incidence of stroke has been declining somewhat 
over the years with better preventive measures, and although one would need to add 
stringent filters to produce quality data, it seems eminently feasible to recruit several 
thousand stroke patients a year if a comprehensive effort were made, and to aim for a 5-year 
sample size in the tens of thousands. That number is large enough that we could apply some 
very powerful machine learning tools to mining structure-function relationships. Of 
necessity, the sample would be fairly diverse, and indeed representative for the population in 
which we want to make clinical predictions. But we could not do it at a single hospital. As 
with fMRI studies, we would need to form consortia that pool data over many sites.
Adolphs Page 2
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Most stroke patients nowadays will get structural scans of their brains that can be used as is, 
and with the ready availability of modern MRI scanners in every major hospital, it would be 
possible to do much better than that. Similarly, most of them will have some cognitive 
assessment, although here there is more need for improvement and administration of a 
standard battery of tasks. If healthcare providers and NIH both saw the value in this, a 
coordinated effort could easily produce research-quality MRI and behavioral data in tens of 
thousands of patients over a few years. Far from becoming obsolete, lesion mapping would 
suddenly offer a wealth of data rivaling, and complementing, that of other large efforts such 
as the human connectome project.
Value to Clinical and Basic Research
Clinical and cognitive neuropsychology part ways in focusing on an assessment of the 
symptoms and prognosis and in inferring something about the way the mind works, 
respectively. The history of cognitive neuropsychology has focused on inferring the 
cognitive architecture of healthy minds from the dissociations that case studies of patients 
with lesions offer (Caramazza, 1986). In this respect, cognitive neuropsychology is similar 
to cognitive neuropsychiatry: both make use of dissociations (deficits in circumscribed 
cognitive domains) to infer causal dependencies between processes. The collection of 
processes and their causal architecture constitutes our best model of the mind and cognition. 
It should be noted that this has not been a common goal of functional neuroimaging, which 
has generally stuck more to describing the neuroscience results than advancing theories of 
cognition (Coltheart, 2006).
But this is changing rapidly. A slew of recent fMRI studies are exploring the isomorphism 
between similarity relationships among psychological constructs (for instance, fear and 
anger may be conceptually more similar to one another than fear and happiness) and 
similarity relationships among patterns of brain activation (for instance, the pattern evoked 
when people think about fear may be more similar to the pattern evoked when people think 
about anger than when people think about happiness). Looking for correspondences between 
the neural representational space as derived from fMRI data and the psychological similarity 
space as derived from theories and ratings of stimuli is indeed giving us some initial tests of 
models of the mind using neuroimaging data (Kriegeskorte and Kievit, 2013). Large-sample 
lesion studies could offer complementary data: when administered the same battery of tasks, 
such data could let us see what dissociations between psychological processes are possible 
and hence serve to separate or combine processes into revised architectures of cognition.
The brain networks we are discovering using resting-state fMRI desperately need some roles 
in cognition. What do they do, and how do they contribute to the mind? The cognitive 
architectures informed by lesion studies desperately need detail and constraint. So we should 
vigorously attempt to merge these two sources of data. Indeed, they can perhaps be merged 
best with data from the very same subjects, since it is eminently feasible to obtain fMRI data 
in patients with focal lesions. Such data could show us how large-scale networks will change 
when one of their nodes is damaged, information critical for understanding the detailed 
causal relations between the nodes. Even if one obtained only resting-state fMRI, given the 
large extant databases on resting-state fMRI that are already available in healthy people 
Adolphs Page 3
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(thousands of subjects) and being accumulated also in psychiatric disorders (e.g., the ABIDE 
network for autism, currently around 500), resting-state fMRI data in lesion patients would 
be a tremendously valuable addition. Are there lesions whose resting-state networks look 
similar to those in autism? Are there some lesions in nodes of healthy resting-state networks 
that severely disrupt those networks, whereas lesions in other nodes can be compensated? 
Does a patient with a neurodegenerative disease show atypical resting-state networks that 
resemble those seen with focal lesions of a particular brain structure, implicating that brain 
structure in the degeneration? The clinical relevance is apparent, but such data would also be 
highly informative for network models of brain function more generally. Causal discovery 
modeling, while in its infancy in cognitive neuroscience, would seem ripe for application to 
such data once sufficiently large datasets have been accumulated (Ramsey et al., 2010; 
Hyttinen et al., 2013). Essentially every network-level fMRI study, which is most fMRI 
studies these days, would benefit if the fMRI study were also carried out in patients who 
have lesions in some of the nodes of the network. One would not even need large samples 
for this approach to be useful (indeed, we have done it with Ns of 2 and 3; Hampton et al., 
2007; Spunt et al., 2015).
These basic-research goals, while of course also informative for clinical questions, would be 
complemented by direct clinical studies. Multivariate classification of the neuroanatomy of 
brain lesions could be used to predict clinical outcome and indeed treatment efficacy. Of the 
800,000 stroke cases a year, only about 140,000 result in death (and that number is declining 
notably over the years with better acute treatment). That leaves most for whom to plan for 
the best care into the future. Redundancy and degeneracy in brain networks, concepts that 
have been around for a while (Price and Friston, 2002) but that have been tested in only a 
few studies so far, could be systematically mapped in large and longitudinal samples. Some 
compensations can be quite dramatic. For example, a rare congenital disorder, the complete 
absence of the entire corpus callosum, remarkably leaves the functional networks seen with 
resting-state fMRI entirely unaffected, including their bilateral symmetry (Tyszka et al., 
2011). This seems to be telling us something important about the functional principles by 
which such networks organize.
It is important to realize that the core data on which lesion studies are based are quite 
objective—we just need good structural scans to map the lesion. Aligning lesions very 
precisely across brains is less easy, but again we can borrow tools from recent developments 
in fMRI, which use multimodal data, including functional data, to improve the alignment. 
There are a host of additional factors that contribute noise and reduce power if not measured, 
but we can incorporate many of those and reduce the effect of the rest through large sample 
sizes. Again, the challenge is not with the lesions themselves—those are as objective and 
close to the brain as you can get. And, more and more, the challenge is also not with the 
analytic tools, since neuroimaging has already done much of the legwork here. The 
challenge is mostly in coordinating a sufficiently large and comprehensive consortium to 
collect the data.
Adolphs Page 4
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The Future
About a quarter of all stroke patients suffer another stroke, and thus a sufficiently large 
longitudinal sample would allow us to progressively map the effects of multiple lesions. Add 
to this the richness of including age among other variables, and it seems clear that an initial 
5-year comprehensive lesion mapping effort would nucleate a much larger set of research 
projects long into the future.
Another important future contribution for lesion studies would be that they lay the 
groundwork and provide a comparison for experimental manipulations of brain function. 
There are a lot of attractive ideas spawned by the BRAIN initiative, but their large-scale 
application in healthy human brains remains unclear. In the meantime, we have naturally 
occurring lesions, and the tools that we will develop for analyzing such data will be 
invaluable also for any future techniques that could experimentally manipulate human brain 
activity. Moreover, many of the same constraints will apply: incorporating background 
variability, designing efficient tasks, and factoring in compensatory and “off-target” effects 
that occur even with the most precise circuit manipulations (Otchy et al., 2015).
Even the most optimistic scenario for the future of lesion studies still leaves us with large 
conceptual challenges that all of cognitive neuroscience faces. What mental functions should 
we try to map onto the brain in the first place? Clearly, not the ones that phrenology had 
proposed, but it remains unclear that our ontology of cognitive processes is quite ready for 
primetime either. There are also deep questions about individual differences. Cognitive 
neuropsychology generally makes a very strong, but seemingly reasonable, assumption 
about cognition: that there is a single, typical, human cognitive architecture. But perhaps the 
mapping from brain function to mind shows more variability, and perhaps the architecture 
does differ substantially across individuals. It is difficult to know whether this would set 
limits to what cognitive neuroscience could contribute to our understanding of the mind, but 
at any rate they would be limits faced by neuroimaging and lesions alike.
Acknowledgments
I thank Antonio Damasio, Hanna Damasio, Daniel Tranel, and Jan Glaescher for helpful comments. Supported in 
part by funding from NIMH Conte Center P50MH094258.
References
Caramazza A. Brain Cogn. 1986; 5:41–66. [PubMed: 3954906] 
Coltheart M. Cortex. 2006; 42:323–331. [PubMed: 16771037] 
Damasio, H., Damasio, AR. Lesion Analysis in Neuropsychology. Oxford University Press; 1989. 
Geschwind, N. Norman Geschwind: Selected Publications on Language, Epilepsy, and Behavior. 
Devinsky, O., Schacter, SC., editors. Butterworth-Heinemann; 1997. 
Hampton AN, Adolphs R, Tyszka MJ, O’Doherty JP. Neuron. 2007; 55:545–555. [PubMed: 
17698008] 
Hyttinen A, Eberhardt F, Hoyer PO. Journal of Machine Learning Research. 2013; 14:3041–3071.
Kriegeskorte N, Kievit RA. Trends Cogn Sci. 2013; 17:401–412. [PubMed: 23876494] 
Mah YH, Husain M, Rees G, Nachev P. Brain. 2014; 137:2522–2531. [PubMed: 24974384] 
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Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, de Ferranti S, Després JP, 
Fullerton HJ, Howard VJ, et al. Stroke Statistics Subcommittee. Circulation. 2015; 131:e29–e322. 
[PubMed: 25520374] 
Otchy TM, Wolff SBE, Rhee JY, Pehlevan C, Kawai R, Kempf A, Gobes SMH, Ölveczky BP. Nature. 
2015; 528:358–363. [PubMed: 26649821] 
Price CJ, Friston KJ. Trends Cogn Sci. 2002; 6:416–421. [PubMed: 12413574] 
Ramsey JD, Hanson SJ, Hanson C, Halchenko YO, Poldrack RA, Glymour C. Neuroimage. 2010; 
49:1545–1558. [PubMed: 19747552] 
Rorden C, Karnath HO. Nat Rev Neurosci. 2004; 5:813–819. [PubMed: 15378041] 
Smith DV, Clithero JA, Rorden C, Karnath HO. Proc Natl Acad Sci USA. 2013; 110:1518–1523. 
[PubMed: 23300283] 
Spunt RP, Elison JT, Dufour N, Hurlemann R, Saxe R, Adolphs R. Proc Natl Acad Sci USA. 2015; 
112:4827–4832. [PubMed: 25825732] 
Tyszka JM, Kennedy DP, Adolphs R, Paul LK. J Neurosci. 2011; 31:15154–15162. [PubMed: 
22016549] 
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Human Lesion Studies in the 21st Century

Human Lesion Studies in the 21st Century
Ralph Adolphs1,*
1Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 
91125, USA
Abstract
The study of patients with brain lesions has made major historical contributions to cognitive 
neuroscience. Here I argue for an increased investment in modern lesion mapping, complementing 
fMRI studies and laying the conceptual and analytic foundations for future techniques that could 
experimentally manipulate human brain function.
The Challenge and Promise of Lesion Studies
Remarkable alterations in cognition and behavior in lesion patients have been widely 
reported and popularized, and are often thought to have been the prerequisite for the 
emergence of cognitive neuroscience (Geschwind, 1997; Damasio and Damasio, 1989). But 
what exactly do such studies tell us, and what place do they find amidst the vastly more 
common approach of functional neuroimaging nowadays?
These questions typically generate two very different sets of answers. One set argues for the 
indispensable value of lesion studies and the dissociations in function they provide. Lesions 
give us insight into the causally necessary function of brain structures, whereas 
electrophysiology and fMRI reflect mere correlations with psychological processes. Lesions 
show us dissociations in cognition we could never have hypothesized, and thus can radically 
change our model of the architecture of the mind. And careful characterization of the deficits 
following lesions and their change over time provides clinically valuable information not 
only about the constellation of impairments produced, but also about their compensation and 
possible resolution over time. When all these pieces are put together with modern tools, data 
from lesion patients seem to show us how dynamic brain networks depend on the function of 
particular components, how cognition can be at least partially decomposed into modules of 
sorts, and how degeneracy and plasticity come into play. In short, lesion studies continue to 
offer unique value for cognitive psychology, cognitive neuroscience, clinical 
neuropsychology, and even cognitive science and philosophy of mind more generally.
A second set of answers is less sanguine. It is instead argued that the famous lesion studies 
can be counted on the fingers of one hand, do not generalize, and were actually wrong in 
most of the details. Furthermore, modern lesion studies are no better: every patient is 
different in idiosyncratic ways, the lesions are far too coarse since they cannot be 
*Correspondence: radolphs@caltech.edu. 
HHS Public Access
Author manuscript
Neuron. Author manuscript; available in PMC 2017 October 25.
Published in final edited form as:
Neuron. 2016 June 15; 90(6): 1151–1153. doi:10.1016/j.neuron.2016.05.014.
A
uthor M
anuscript
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experimentally produced, and fMRI has already made the lesion method obsolete (see 
Rorden and Karnath, 2004 for a synopsis of these views and counterarguments).
The far more recent history of fMRI research has had its own share of ups and downs in the 
public image, but nobody concluded from debates about statistical or other technical 
difficulties that we should abandon fMRI. A huge effort has instead been put into developing 
more and more refined tools and into accumulating large shared datasets for fMRI analyses. 
Indeed, much of this development has in fact been driven by the temporary crises around 
replication and statistical power that the field has weathered. Many of the same problems 
apply to lesion studies. Happily, so do many of the same solutions. We need better statistical 
reliability and generalizability, more complex models that consider (for example) white 
matter and gray matter separately, and multivariate analyses. But these are all eminently 
surmountable problems. There is no inherent limitation here.
So one answer is that the criticisms outlined above are outdated. Yes, classical studies had 
single cases and were crude in many ways (so, too, were early neuroimaging studies), but 
these are problems that should be and can be solved, not inherent barriers to the method. For 
example, new multivariate algorithms for lesion-symptom mapping are now being applied 
(Smith et al., 2013; Mah et al., 2014). These algorithms can build in sophisticated 
information about the statistical anatomical dependencies between voxels in lesion analysis. 
In the case of a stroke, for example, the likelihood of different voxels being lesioned or not 
is highly correlated across an anatomically complex shape that results from how blood 
vessels are distributed in the brain. Multivariate algorithms can incorporate this information 
into their analysis, can treat gray and white matter separately, and can look for distributed 
patterns of lesions as the best correspondence to a cognitive process. These approaches 
address many of the methodological complaints that have been leveled against lesion 
mapping in the past. Another difficulty is that the lesion interacts with many other variables
—so we need models that incorporate these other variables (age, sex, IQ, etc.). Such more 
complex models are also straightforward to construct in principle. However, these advances 
all require one key ingredient: a very large sample size. Even hundreds of lesion patients 
won’t do it; we need more than that. But there is actually no shortage of potential data at all. 
The challenge is how to collect and coordinate it.
It is estimated that there are about 800,000 inpatients a year in the U.S. with a primary 
diagnosis of stroke (Mozaffarian et al., 2015). There are a smaller number with brain injury 
from other causes like tumors or epilepsy surgery, which offer complementary strengths and 
confounds for comparison. Although the incidence of stroke has been declining somewhat 
over the years with better preventive measures, and although one would need to add 
stringent filters to produce quality data, it seems eminently feasible to recruit several 
thousand stroke patients a year if a comprehensive effort were made, and to aim for a 5-year 
sample size in the tens of thousands. That number is large enough that we could apply some 
very powerful machine learning tools to mining structure-function relationships. Of 
necessity, the sample would be fairly diverse, and indeed representative for the population in 
which we want to make clinical predictions. But we could not do it at a single hospital. As 
with fMRI studies, we would need to form consortia that pool data over many sites.
Adolphs Page 2
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Most stroke patients nowadays will get structural scans of their brains that can be used as is, 
and with the ready availability of modern MRI scanners in every major hospital, it would be 
possible to do much better than that. Similarly, most of them will have some cognitive 
assessment, although here there is more need for improvement and administration of a 
standard battery of tasks. If healthcare providers and NIH both saw the value in this, a 
coordinated effort could easily produce research-quality MRI and behavioral data in tens of 
thousands of patients over a few years. Far from becoming obsolete, lesion mapping would 
suddenly offer a wealth of data rivaling, and complementing, that of other large efforts such 
as the human connectome project.
Value to Clinical and Basic Research
Clinical and cognitive neuropsychology part ways in focusing on an assessment of the 
symptoms and prognosis and in inferring something about the way the mind works, 
respectively. The history of cognitive neuropsychology has focused on inferring the 
cognitive architecture of healthy minds from the dissociations that case studies of patients 
with lesions offer (Caramazza, 1986). In this respect, cognitive neuropsychology is similar 
to cognitive neuropsychiatry: both make use of dissociations (deficits in circumscribed 
cognitive domains) to infer causal dependencies between processes. The collection of 
processes and their causal architecture constitutes our best model of the mind and cognition. 
It should be noted that this has not been a common goal of functional neuroimaging, which 
has generally stuck more to describing the neuroscience results than advancing theories of 
cognition (Coltheart, 2006).
But this is changing rapidly. A slew of recent fMRI studies are exploring the isomorphism 
between similarity relationships among psychological constructs (for instance, fear and 
anger may be conceptually more similar to one another than fear and happiness) and 
similarity relationships among patterns of brain activation (for instance, the pattern evoked 
when people think about fear may be more similar to the pattern evoked when people think 
about anger than when people think about happiness). Looking for correspondences between 
the neural representational space as derived from fMRI data and the psychological similarity 
space as derived from theories and ratings of stimuli is indeed giving us some initial tests of 
models of the mind using neuroimaging data (Kriegeskorte and Kievit, 2013). Large-sample 
lesion studies could offer complementary data: when administered the same battery of tasks, 
such data could let us see what dissociations between psychological processes are possible 
and hence serve to separate or combine processes into revised architectures of cognition.
The brain networks we are discovering using resting-state fMRI desperately need some roles 
in cognition. What do they do, and how do they contribute to the mind? The cognitive 
architectures informed by lesion studies desperately need detail and constraint. So we should 
vigorously attempt to merge these two sources of data. Indeed, they can perhaps be merged 
best with data from the very same subjects, since it is eminently feasible to obtain fMRI data 
in patients with focal lesions. Such data could show us how large-scale networks will change 
when one of their nodes is damaged, information critical for understanding the detailed 
causal relations between the nodes. Even if one obtained only resting-state fMRI, given the 
large extant databases on resting-state fMRI that are already available in healthy people 
Adolphs Page 3
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(thousands of subjects) and being accumulated also in psychiatric disorders (e.g., the ABIDE 
network for autism, currently around 500), resting-state fMRI data in lesion patients would 
be a tremendously valuable addition. Are there lesions whose resting-state networks look 
similar to those in autism? Are there some lesions in nodes of healthy resting-state networks 
that severely disrupt those networks, whereas lesions in other nodes can be compensated? 
Does a patient with a neurodegenerative disease show atypical resting-state networks that 
resemble those seen with focal lesions of a particular brain structure, implicating that brain 
structure in the degeneration? The clinical relevance is apparent, but such data would also be 
highly informative for network models of brain function more generally. Causal discovery 
modeling, while in its infancy in cognitive neuroscience, would seem ripe for application to 
such data once sufficiently large datasets have been accumulated (Ramsey et al., 2010; 
Hyttinen et al., 2013). Essentially every network-level fMRI study, which is most fMRI 
studies these days, would benefit if the fMRI study were also carried out in patients who 
have lesions in some of the nodes of the network. One would not even need large samples 
for this approach to be useful (indeed, we have done it with Ns of 2 and 3; Hampton et al., 
2007; Spunt et al., 2015).
These basic-research goals, while of course also informative for clinical questions, would be 
complemented by direct clinical studies. Multivariate classification of the neuroanatomy of 
brain lesions could be used to predict clinical outcome and indeed treatment efficacy. Of the 
800,000 stroke cases a year, only about 140,000 result in death (and that number is declining 
notably over the years with better acute treatment). That leaves most for whom to plan for 
the best care into the future. Redundancy and degeneracy in brain networks, concepts that 
have been around for a while (Price and Friston, 2002) but that have been tested in only a 
few studies so far, could be systematically mapped in large and longitudinal samples. Some 
compensations can be quite dramatic. For example, a rare congenital disorder, the complete 
absence of the entire corpus callosum, remarkably leaves the functional networks seen with 
resting-state fMRI entirely unaffected, including their bilateral symmetry (Tyszka et al., 
2011). This seems to be telling us something important about the functional principles by 
which such networks organize.
It is important to realize that the core data on which lesion studies are based are quite 
objective—we just need good structural scans to map the lesion. Aligning lesions very 
precisely across brains is less easy, but again we can borrow tools from recent developments 
in fMRI, which use multimodal data, including functional data, to improve the alignment. 
There are a host of additional factors that contribute noise and reduce power if not measured, 
but we can incorporate many of those and reduce the effect of the rest through large sample 
sizes. Again, the challenge is not with the lesions themselves—those are as objective and 
close to the brain as you can get. And, more and more, the challenge is also not with the 
analytic tools, since neuroimaging has already done much of the legwork here. The 
challenge is mostly in coordinating a sufficiently large and comprehensive consortium to 
collect the data.
Adolphs Page 4
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The Future
About a quarter of all stroke patients suffer another stroke, and thus a sufficiently large 
longitudinal sample would allow us to progressively map the effects of multiple lesions. Add 
to this the richness of including age among other variables, and it seems clear that an initial 
5-year comprehensive lesion mapping effort would nucleate a much larger set of research 
projects long into the future.
Another important future contribution for lesion studies would be that they lay the 
groundwork and provide a comparison for experimental manipulations of brain function. 
There are a lot of attractive ideas spawned by the BRAIN initiative, but their large-scale 
application in healthy human brains remains unclear. In the meantime, we have naturally 
occurring lesions, and the tools that we will develop for analyzing such data will be 
invaluable also for any future techniques that could experimentally manipulate human brain 
activity. Moreover, many of the same constraints will apply: incorporating background 
variability, designing efficient tasks, and factoring in compensatory and “off-target” effects 
that occur even with the most precise circuit manipulations (Otchy et al., 2015).
Even the most optimistic scenario for the future of lesion studies still leaves us with large 
conceptual challenges that all of cognitive neuroscience faces. What mental functions should 
we try to map onto the brain in the first place? Clearly, not the ones that phrenology had 
proposed, but it remains unclear that our ontology of cognitive processes is quite ready for 
primetime either. There are also deep questions about individual differences. Cognitive 
neuropsychology generally makes a very strong, but seemingly reasonable, assumption 
about cognition: that there is a single, typical, human cognitive architecture. But perhaps the 
mapping from brain function to mind shows more variability, and perhaps the architecture 
does differ substantially across individuals. It is difficult to know whether this would set 
limits to what cognitive neuroscience could contribute to our understanding of the mind, but 
at any rate they would be limits faced by neuroimaging and lesions alike.
Acknowledgments
I thank Antonio Damasio, Hanna Damasio, Daniel Tranel, and Jan Glaescher for helpful comments. Supported in 
part by funding from NIMH Conte Center P50MH094258.
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Human Lesion Studies in the 21st Century

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