Can the connection between psychology and neuroscience provide a
sufficient framework to support the study of the development of
maladaptation and psychopathology? This Special Issue is devoted to papers
that address this general issue within their specific domains. If we hope
to provide a definitive answer to the question posed above, then it is
important to know how cognitive and affective neuroscience arose, what are
their distinctive findings to date, and, to the extent possible, predict
what future developments can be expected. Before proceeding, we first
examine the basic principles inherent to a developmental psychopathology
perspective, as well as the multiple disciplines that played a critical
role in its evolution as an interdisciplinary science.
Principles of Developmental
Psychopathology
Historically, scientists in a variety of disciplines, including
genetics, biology, neuroscience, embryology, psychology, and psychiatry,
have stressed the importance of examining the interrelation between normal
and abnormal patterns of development (see Cicchetti, 1990, for a historical review). Implicit in this
perspective is an underlying commitment to understanding normal
developmental processes so that we can begin to investigate the ways in
which deviant development may eventuate. Furthermore, the examination of
abnormal developmental processes and of the deviations from normal
pathways of development may illuminate the range of individual variation
inherent in the human organism with respect to neurobiological, cognitive,
and affective functioning.
In part, as an outgrowth of these historical influences, over the
course of the past several decades, developmental psychopathology has
emerged as an integrative scientific discipline that strives to unify,
within a lifespan framework, contributions from multiple fields of inquiry
with the goal of understanding the relation between psychopathology and
normative adaptation (Cicchetti, 1984; Cicchetti
& Cohen, in press-a, in
press-b, in press-c; Rutter & Garmezy, 1983; Sroufe
& Rutter, 1984). Since its inception, work conducted within a
developmental psychopathology perspective has incorporated theory and
research from the fields of normal and abnormal development and advocated
multidisciplinary approaches in its emphasis on examining the mutual
interplay between normality and psychopathology with the ultimate goal of
understanding individual patterns of adaptation and maladaptation
(Cicchetti, 1984, 1990; Cicchetti & Toth,
1991; Sroufe & Rutter, 1984).
In recent years, developmental psychopathologists have increasingly
acknowledged that the investigation of developmental processes, both
normal and atypical, is an inherently interdisciplinary enterprise.
Scientists must utilize different levels and methods of analysis,
depending on the questions being addressed in their research. Ideally,
investigations must direct their energies toward an examination of
multiple levels of analysis within the same individual. Although some
problems are best addressed with the concepts and methods of a single
discipline, other issues require interdisciplinary integration. In fact,
history reveals that disciplines themselves often evolve from
interdisciplinary efforts. For example, neuroscience developed as
researchers working in a number of fields began to work in concert to
solve some of the common scientific mysteries that existed about the
nervous system (Albright, Jessell, Kandel, &
Posner, 2000; Cowan, Harter, & Kandel,
2000). Importantly, the principles that neuroscientists have
discovered have been utilized to inform research on the development of
maladaptation and psychopathology (see, e.g., Cicchetti
& Cannon, 1999; Cicchetti & Walker, 2001, 2003).
It is apparent from the questions addressed by developmental
psychopathologists that progress toward a process-level understanding of
maladaptive and psychopathological outcomes will require research designs
and strategies that call for the simultaneous assessment of multiple
domains of variables, both within and outside of the developing person
(Cicchetti & Dawson, 2002). To comprehend
psychopathology fully, all levels of analysis must be examined and
integrated. Such research, almost by its very nature, must be
interdisciplinary.
The National Advisory Mental Health Council (2000) recently concluded that interdisciplinary
research should be accorded a higher priority in the basic science
portfolio of the National Institute of Mental Health. Similarly, an
Institute of Medicine report (Pellmar & Eisenberg,
2000) noted that it was critical to bridge research across
disciplines to formulate a more comprehensive understanding of high-risk
conditions and mental disorders. The power embodied by cross-disciplinary
investigations that utilize multiple levels of analysis methodologies
promises to significantly strengthen our capacity to decrease the burden
of mental illness for society.
In this Special Issue, we examine the contribution that two prominent
fields of neuroscience research can make to understanding the development
of maladaptation and psychopathology. Specifically, we invited scientists
working in the areas of cognitive and affective neuroscience to provide
illustrations of how research in these two disciplines could elucidate the
developmental processes eventuating in maladaptive or pathological
outcomes.
Cognitive and Affective Neuroscience
The idea of connecting mental processes to brain activity goes back to
the very beginning of psychology (Boring, 1950).
The connection certainly received a considerable boost with the
publication of Hebb's book, Organization of Behavior (1949), with its neural network approach to cognitive
processes. Modern versions of cognitive neuroscience arose from the
development of neuroimaging methods to view changes in the brain during
thought processes such as occur during laboratory tasks (Albright, Kandel, & Posner, 2000; Gazzaniga, 2004; Posner &
DiGirolamo, 2000). Imaging, first with positron emission tomography
(Posner & Raichle, 1994), and later with
functional magnetic resonance imaging (Rosen, Buckner,
& Dale, 1998), opened up the human brain to detailed empirical
examination of the nature and development of the neural networks Hebb
proposed (for a recent review of this connection, see Posner & Rothbart, 2004). When combined with
electrical or magnetic recording from outside the skull, it became
possible to visualize in real time the circuits involved in competing
aspects of an experimental task. The earliest imaging studies of language
and attention fit quite naturally into the cognitive psychology and
neuroscience frameworks and led to the development of journals and
societies with the name cognitive neuroscience.
The study of the processing of positive and negative affect was also
an important issue at the start of the 20th century (Dagleish, 2004; James, 1890;
Panksepp, 1998). Emotion was even more closely
connected to physiology through animal research than was cognition. It was
natural that neuroimaging would also take up issues of emotion (Davidson, 2000; Davidson, Jackson,
& Kalin, 2000; Davidson, Scherer, &
Goldsmith, 2003) and questions related to understanding the minds
and emotions of others (Ochsner, 2004).
Affective, social, and cognitive neuroscience have been built upon similar
methods of neuroimaging applied to somewhat different problems. Moreover,
the results of studies in all three areas have usually suggested specific
networks of neural areas related to different cognitive, emotional, and
social functions. These findings have the potential to draw the areas of
social, affective, and cognitive studies closer together. For example,
networks of brain areas related to regulation of cognitive and emotional
functions have been shown to involve adjacent areas within the frontal
midline (Bush, Luu, & Posner, 2000).
Processing physical pain and the pain of social rejection appeared to
involve overlapping areas of the anterior cingulate (Eisenberger, Lieberman, & Williams, 2003).
Although anxiety and error can produce similar areas of activity in both
cognitive and emotional tasks, many areas in the networks involved in
emotion also can be quite distinct from those involved in unemotional
cognitive tasks. The common network approach to diverse areas of
psychology is beginning to lead to other commonalities in their study. For
example, neural network models can be applied to cognitive, emotional
(McClelland, 2001), or social psychological
topics (Keysers & Perrett, 2004).
Although both cognitive and emotional tasks have revealed networks of
brain areas common to most or all persons, there are also individual
differences in details that influence the efficiency of the networks
operation. These differences are likely to reflect both genes and
experience. The rapid development of functional magnetic resonance imaging
methods has begun to provide a basis for understanding differences among
individual brains both anatomically and in terms of functional
activations. Several studies have shown that individual differences in
functional activation can be reliably assessed (Miller,
Van Horn, Wolford, Handy, Valsangkar–Smyth, Inati, Grafton, &
Gazzaniga, 2002; Ress, Backus, & Heeger,
2000).
These differences are to be expected because people are not identical
in their thoughts, feelings, or behaviors. Studies also have examined the
role of genetic differences in the strength of activation of networks
involved in attention and memory (Goldberg &
Weinberger, 2004). For example, studies in animals have identified
a gene called brain-derived neurotrophic factor (BDNF) that plays a
crucial role in long-term potentiation, thought to be a model of memory.
One study examined the role of differences in two forms (alleles) of the
BDNF gene (Egan et al., 2003). The
behavioral part of the study compared performance of two groups with
different forms of the gene performing a test of learning and memory. The
two groups performed differently on the test, with the difference among
alleles accounting for about 25% of individual differences on the test.
When the test was run in the brain scanner on much smaller groups,
significant differences between them were found in the hippocampus.
Because the hippocampus is an important node in the network underlying
explicit storage and retrieval in memory, these findings supported the
importance of the BDNF gene in that function.
Work on attention has measured individual differences using the
Attention Network Test (Fan, McCandliss, Sommer, Raz,
& Posner, 2002). The test provides a measure of the efficiency
of attentional networks related to maintaining the alert state, orienting
to sensory information, and controlling conflict between competing
responses. Studies of alert monkeys have shown that the orienting system
is modulated by cholinergic input (Davidson &
Marrocco, 2000) whereas dopamine is the principle modulator of the
frontal areas important for monitoring conflict. Alleles of two
cholinergic genes have been found to influence a visual search task
related to the orienting network (Parasuraman,
Greenwood, Kumar, & Fossella, in press); however, alleles of
dopamine genes influence performance in the flanker task and, when
compared, produced a significant difference in activation in the anterior
cingulate (Diamond, Briand, Fossella, & Gehlbach,
2004; Fan, Fossella, Summer, & Posner,
2003).
These studies demonstrate that at least part of the variability in
strength of activation is due to having different versions (alleles) of
genes related to the network. However, genetic differences observed to
date account for only a part of the variance found in behavior and
imaging. Another potential contribution of the observed differences is
that they serve as clues to the genes involved in the development of the
common network. The genes related to individual differences can be
examined in comparative animal studies to address questions such as how
genes related to hippocampal development may have affected behavior in
species even before there was a hippocampus and, in species for which the
hippocampus plays a role in forms of memory, how these genes may be
precursors of the explicit recollection found to be its role in humans. In
the case of the DRD4 gene, which in humans is related to
attention deficit disorder (Swanson, Deutsch, Cantwell,
Posner, Kennedy, Barr, Moyzis, Schuck, Flodman, & Spence,
2001), and a different allele to the normal monitoring of conflict
(Fan et al., 2002), deletion of this gene in the
mouse seems to be related to exploration of the environment (Grandy & Kruzich, 2004). These studies have the
potential of improving our understanding of the role of genes in shaping
the networks common to all humans. They also may eventually help us to
understand how pathologies are related to genetic differences among
people.
Now that the sequencing of the entire human genome has been completed
(Venter, Adams, Myers, Li, Mural, Sutton, Smith,
Yandell, & Evans, 2001), it is possible not only to examine the
functional anatomy of brain networks but also to investigate how genetic
differences might contribute to individual variation in the potential to
use these neural networks in the acquisition and performance of skills.
Nonetheless, the developmental pathway from genetic endowment to normal or
abnormal performance will neither be simple nor independent from an
understanding of the neural networks themselves.
The advent of contemporary cognitive and affective neuroscience and
the effective integration and synthesis of the many previously independent
disciplines that currently comprise the field (Albright
et al., 2000; Albright, Kandel, et al.,
2000; Cicchetti, 2002; Cicchetti & Cannon, 1999) also represent an
unprecedented opportunity to augment current conceptual and methodological
approaches to the study of resilience (Charney,
2004; Curtis & Cicchetti, 2003; Davidson, 2000). In addition, basic cognitive and
affective neuroscience research on the development of maladaptation and
psychopathology can be used to inform the diagnosis, prevention, and
treatment of mental disorders. Such “translational research”
(National Advisory Mental Health Council, 2000)
is in direct accord with two of the major tenets of a developmental
psychopathology perspective, namely, the reciprocal interplay between
basic and applied research, and between normal and atypical development
(Cicchetti, 1990; Cicchetti
& Toth, 1998).
An outstanding example of the ability to use neural networks to
approach issues of clinical treatment has emanated from studies of
depression (Goldapple, Segal, Garson, Lau, Bieling,
Kennedy, & Mayberg, 2004; Mayberg,
2003). These investigations have examined clinical interventions
for depression based upon a neural network model. Treatments have involved
drugs or cognitive–behavioral therapy. Both forms of therapy have
been shown to be about equally effective based on percentages of persons
showing improvement. However, neuroimaging data showed that the two
therapies involved very different neural networks. The drugs remediated a
largely subcortical network of brain areas that might be difficult to
control voluntarily. The cognitive–behavioral therapy worked on
cortical networks including areas involved in attention that would be more
easily subject to voluntary control. These findings also suggest how
genetic studies might allow better fits between the therapy used and the
individual (Cicchetti & Blender, 2004).
Another example of the use of neuroimaging is in the study of reading.
Dyslexia can be defined as a low level of reading skill that cannot be
accounted for by general intelligence or poor educational opportunity
(Shaywitz, 2003). Imaging studies have shown
dyslexia to involve under activation in two brain areas important in
normal reading, a posterior phonological area, and an area of the visual
system called the visual word form area. The visual word form area is
thought to be involved in chunking the letters into a unified whole. In
normal readers, these two areas appear to work automatically to convert
visual words to appropriate sounds, but in dyslexic children they show
little or no activation until after training. It appears that phonological
training may be an optimal way to induce activation of the phonological
area, but we do not know what methods will prove optimal for training of
visual word form. One study did show a change in the word form area after
1 year of phonological training (Shaywitz et al.,
2004), but we do not know whether it was the phonology or extra
reading that was most effective in the change. One set of studies suggests
that word form activation in children occurs only for words that the child
already knows, while in adults, activation is based upon orthography and
shows similar activation for unfamiliar but pronounceable nonwords as it
does for familiar words (Posner & McCandliss,
1999). This might suggest that word form development occurs as a
part of practice in reading.
These findings show how important imaging the underlying neural
network is likely to be in improving the effectiveness of therapies and in
adapting them to prevention and to individual needs. Furthermore, the
conduct of basic cognitive and affective neuroscience investigations of
biological and psychological processes contributing to resilient
functioning, in concert with the examination of the biological and
psychological changes that occur as a function of resilience-promoting
interventions, will greatly enhance our present knowledge base on the
development of resilience and on the recovery of positive function.
In summary, the papers in this Special Issue serve to highlight the
synergy that exists between cognitive and affective neuroscience and
developmental psychopathology. To fully realize the potential that the
cross-fertilization of these disciplines may yield, we must make an
ongoing commitment to fostering interdisciplinary work addressing multiple
levels of analysis. The potential for these endeavors to decrease the
burden of mental illness is vast and therefore warrants the dedication of
professionals, as well as the allocation of resources.
