Functional Connectivity: Probing the Brain’s Astounding Complexity

February 23, 2018
Barbara Schildkrout, MD
Volume 35, Issue 2

Functional connectivity is a “rapidly developing scientific story.” And for psychiatrists, it is a story worth following.

One of the greatest scientific challenges for 21st century medicine is to illuminate the relationship between the brain and what we call “mind.” Psychiatrists want to know how we get from neurons and synapses to mental suffering. How do learning, development, and cognitive flexibility arise? What accounts for the uniqueness of every human self? What goes wrong in psychiatric disease? Within the psychiatric community, brain science has generally been regarded as being still too elementary to explain such complex phenomena.

In this article I describe how analysis of the brain’s intrinsic functional connectivity has become an important approach for expanding our understanding of the astounding complexity of the human brain. Utilizing this new paradigm, it is possible to explore questions that earlier seemed virtually unfathomable; many of these are relevant, even pivotal, to psychiatry.

In news parlance, functional connectivity is a “rapidly developing scientific story.” And for psychiatrists, it is a story worth following.

What is functional connectivity?

Functional “imaging” measures physiological factors that are considered to be a gauge of neuronal functioning (such as changes in regional oxygen utilization); the accumulated data are then transformed into “human readable” images. Functional “connectivity” utilizes functional imaging data and analyzes the statistical associations between measurements of neurophysiological activity in 2 or more spatially remote areas of the brain. Functional connectivity studies are a mathematical, non-theoretical look at activity over the whole brain, in an attempt to discern in which areas the activity is either correlated or anti-correlated. For example, do areas A and X display increased metabolic activity when area D exhibits decreased metabolic activity?

Traditional functional connectivity methods do not tell us about the direction of connectivity-which region is influencing which. Functional connectivity also does not tell us whether 2 regions are simultaneously being influenced by a third. Nor does functional connectivity say anything about the way in which various brain areas might be structurally connected. Functional connectivity takes a purely statistical look at larger patterns of neurophysiologic activity that emerge from the brain’s hundreds of billions of neurons, reciprocally interacting over both short and long distances at microsecond speed.

Early functional connectivity research was designed to study the brain while an individual was performing a task or interacting with the environment-termed “psychophysiological interactions.” More recent work has shifted to a focus on the brain’s intrinsic neural activity while the subject is at “rest” or engaged in undirected thought.

The historical context

It was not until the early 1800s that modern scientific notions about the brain began to take precedence.2 Franz Joseph Gall promoted the idea that various mental capacities were localized to different brain regions; he also believed that the strength of these qualities could be measured by looking at protrusions in the skull overlying these areas. This notion was at the heart of phrenology, extremely popular in the 19th century.

Gall assumed that the brain was symmetrically organized. His categorization of what constituted fundamental, functional capacities of the brain drew on cultural values of his day, including vanity, guile, kindness, and pride. Although scientific advances have discredited many of these claims, Gall’s fundamental insight about localization was profound.

Localization was a powerful new idea at the time; it exhibited the 2 fundamental attributes of a paradigm as first defined by the science historian Thomas S. Kuhn as “. . . sufficiently unprecedented to attract an enduring group of adherents away from competing modes of scientific activity. Simultaneously, it was sufficiently open-ended to leave all sorts of problems for the redefined group of practitioners to resolve.”3

The localization paradigm opened new questions for legitimate scientific inquiry; neurological investigators became the Lewis and Clark of neuro-anatomic territories. More than 200 years of explorations in neurology have been spent mapping the localization of various functional elements within the brain and simultaneously parsing brain activity into its most fundamental capabilities.

For example, until the mid-1950s, memory was considered to be a widely distributed, unitary function of the brain. Then, studies of patient H. M. elucidated a central role for the hippocampus in memory formation. In addition, episodic and procedural memory processes were differentiated.

While the concept of localization drove much of the work of scientific exploration during this time, there also were global, overarching theories of brain function. The eminent British neurologist John Hughlings Jackson posited that the more evolutionarily developed regions of the brain exerted control over primitive brain areas; he articulated how disturbances in this organization were evident in disease states. Indeed, many scientists who are well known for their work on localization (Wernicke, Penfield) also were aware that the brain was extraordinarily complex and that the parts had to work together, like an astoundingly accomplished orchestra.

Structural connectivity

An important step in expanding the localization concept and focusing on how various parts of the brain communicated was the recognition of disconnection syndromes in the 1960s.4 Disconnection syndromes did not result from lesions in the cortical gray matter itself but rather from interruptions in the white matter fibers and tracts running to and from cell bodies in gray matter-the lines of communication within the brain. If a lesion in white matter isolates or “disconnects” a crucial cortical region, this could lead to a clinical picture similar to one seen with a lesion in the cortical area itself. In other words, now neurologists were turning their attention to structural connectivity, the so-called wiring diagram of the brain.

Astoundingly, all of this work was accomplished by utilizing only clinical observation, scientific reasoning, and post-mortem findings. The only imaging available was pneumoencephalography, a painful technique that allowed clinicians to visualize the shape of the ventricles in the brain of a living patient by using x-rays after injecting air into the spinal column.

Structural brain imaging

Until computed tomography (CT) came into clinical use in 1973, it was not possible to visualize brain parenchyma during life. Not until 1994 was there a technique that spared radiation exposure-magnetic resonance imaging (MRI). CT, MRI, and other brain imaging approaches have allowed us to make remarkable advances in mapping human brain structures and in diagnosing human disease, but these modalities also have inherent limitations. An MRI scan is not a “picture” in the usual sense but rather a computer-generated readout of summary information into a digital image. One voxel (essentially a 3-dimensional pixel) in brain imaging contains information from approximately 1 cubic millimeter. Within 1 cubic millimeter of gray matter are perhaps a million neurons plus glia cells, blood vessels, and extracellular space.5

In other words, none of these structural imaging techniques approaches the level of the individual neuron. Also, a structure-alone perspective of the human brain has other inherent limitations. For example, even if we take into account ongoing remodeling of neuronal connections (neuroplasticity), how can we account for the brain’s astounding moment-to-moment flexibility?

Functional brain imaging

Functional imaging techniques measure fluctuations over time in factors that gauge neuronal activity such as regional glucose utilization, blood flow, or oxygen consumption. The most widely used functional imaging techniques are positron emission tomography (PET), which utilizes radioactively tagged molecules, and functional MRI (fMRI), which differentiates molecules by their behavioral responses within a magnetic field.

One important functional imaging approach is based on the observation that, in a magnetic field, oxygenated blood behaves differently than de-oxygenated blood. This allows researchers to track fluctuations in blood-oxygen levels in human subjects in the scanner without the use of radiation. This blood-oxygen level dependent (BOLD) signal has become a major investigative tool.

Functional imaging techniques allow clinicians to evaluate patients who may have deficits in neuronal functioning even when structural deficits are not apparent. For example, early in the disease course of frontal dementias, patients may have decreased functioning in frontal regions before any changes are seen on structural imaging.

Functional imaging also has made it possible for investigators to pursue a myriad of fundamental questions. One line of such inquiry examines which parts of the brain are active as someone performs a carefully designed task. For instance, how is viewing a familiar face different from viewing an unfamiliar one? Which parts of the brain are involved in moral decision-making? Also, for a given task, are normal individuals functionally different from people who have specific psychiatric conditions?

Limitations of functional imaging

A serious obstacle for functional imaging research has been distinguishing a “task signal” of significance from background “noise.” The brain is metabolically very active, utilizing approximately 20% of body energy resources even though it represents only 2% of body mass.5 Most of this energy utilization is from ongoing neuronal metabolism. When performing a demanding cognitive task, the brain’s energy utilization increases by less than 5%. Furthermore, differences that might exist between the normal and the study populations are even smaller.6 These factors make the task signal difficult to detect.

Also, individuals vary in their level of effort, degree of anxiety associations related to the task that come to consciousness, movement during the study, and so on. Although these brain-based activities are not the focus of the study, the metabolic activity they produce shows up in the scanner. Therefore, to amplify the task signal and also draw broad conclusions, a widely used approach to studying task-related questions has been to pool the findings from numerous individuals and average the results onto a standard anatomical brain atlas.

Along with these technical challenges, functional imaging studies also have a theoretical limitation built into their fundamental design. Task-based functional connectivity studies focus on the brain correlates of the task and assume the brain’s ever-present background neuronal metabolic activity is simply “noise.”

Resting state connectivity and its importance

In 2001, an important observation changed the field of functional imaging. Marcus Raichle compared PET and, later, fMRI BOLD signal findings from research subjects who had been engaged in task-based studies with those who were in control groups. Dr. Raichle’s laboratory routinely used the “rest condition” as a control rather than using, for example, a neutral task as a control for an emotional one.

At some point in our work, and I do not recall the motivation, I began to look at the resting state scans minus the task scans. What immediately caught my attention was the fact that regardless of the task under investigation, activity decreases were clearly present and almost always included the posterior cingulate and the adjacent precuneus. . . . Initially puzzled by the meaning of this observation, I began collecting examples from our work and placed them in a folder which I labelled [sic] MMPA for mystery medial parietal area.7

Further analysis of data by Marcus Raichle and his colleagues led to the identification of a network of specific brain regions in which activity was anti-correlated with task-based activity no matter what the task was. This network was named the Default Mode Network (DMN) by Michael D. Greicius.8 The work of Raichle and others was consistent with the first published report of intrinsic resting state functional connectivity by Biswal and colleagues in 1995 that “functionally related brain regions exhibited correlation of low frequency fluctuations in the resting state.”9

The importance of these discoveries has been far-reaching

Consider that previously, in task-based functional imaging studies, the challenge had been to find the signal within the experimental background “noise.” Now it had become clear that this “noise” was data. The fluctuating BOLD signal could be mathematically mined as a source of information about the intrinsic functional organization of the brain. Moreover, the data could be obtained relatively easily by placing a person in a scanner and instructing him or her to “rest” or to visually fixate on 1 spot: this made it possible to study some patients who had difficulty cooperating with other protocols.

Moreover, using functional connectivity did not require patient averaging. Subjects could be studied individually, making it possible to compare different individuals or 1 individual at different times. These advantages made the prospect of utilizing functional connectivity as a clinical tool more viable.

For researchers, “. . . functional brain connectivity . . . [had] become one of the most influential concepts in modern cognitive neuroscience, especially given the current shift in emphasis from studies of functional segregation to studies of functional integration.”10 We had long appreciated that detailing the synapse-to-synapse, structural organization of the brain would not capture the brain’s vast neuronal networks at work, operating as a dynamic system at speeds that would support the myriad manifestations of complex human behavior. Now “. . . task-free analysis of intrinsic connectivity networks may help elucidate the neural architectures that support fundamental aspects of human behavior.”11

What neuroscience has learned from studies of functional connectivity

The DMN is only one of numerous large-scale, intrinsically synchronized, dynamic and interacting, functionally organized networks in the brain. These intrinsic functional networks and important nodes or hubs in those networks are consistent with synaptic maps of the brain. In other words, these networks do not violate our previous understanding of the anatomical organization of the brain into systems for motor behavior, perception, cognition, and so on.

The intrinsic functional networks can be found during cognitive tasks and in the rest condition, even when the systems for motor behavior, perception of various kinds, cognition, etc, are not being consciously engaged. Indeed, these networks persist during sedation, sleep, and under anesthesia.

While the intrinsic functional networks agree with earlier understanding of anatomical organization, the networks are not restricted to neuroanatomical regions with single synapse connections. The astounding degree of structural neural-network complexity in the brain likely explains how regions of the brain might be “functionally connected” even when their “structural connections” are not clear.

There is rapid coordination and interaction among the intrinsic brain networks and their hub regions. The brain is constantly switching connectivity patterns and reorganizing according to demands of the moment. Although the brain is a massively complex dynamic system, it can be studied by utilizing advanced imaging techniques and innovative mathematical and computational approaches. The importance of collaboration between experts in different fields cannot be overstated.

The most studied networks that relate to cognition are the Central Executive Network (CEN), a Salience Network (SN), and the DMN. The rapid interplay of these and perhaps other networks underpins behavioral changes that are based on the individual’s homeostatic needs, given that conditions (both internal and external) shift rapidly. The CEN is most active during cognitive tasks. The SN is activated in response to salient stimuli and plays a role in emotional processing and in switching from the DMN to the CEN. These networks are found in everyone; however, there is individual variation in features such as the strength of connectivity within each network.

The most far-reaching question we posed was whether regional functional connectivity within the salience and executive-control networks in the task-free setting would correlate with subject attributes measured outside the scanner. In other words, do individual differences in intrinsic connectivity strength correlate with how one feels and thinks in daily life?11

This idea has been used to look at a large variety of traits and conditions. Indeed, individual variations in the coherence of these intrinsic networks appear to correlate with patient capabilities (eg, fluid intelligence, stressor-associated anticipatory anxiety, executive task performance).11,12 Should studies confirm that variations in functional connectivity are reliable indicators of specific behavioral variations outside the scanner, the potential clinical uses of this technology are staggering.

Theoretically, the mechanisms by which genetic variation, maturation, and experience affect an individual may be understood within this paradigm of intrinsic functional network organization in the brain. Early variations in neural development as well as experiences over time have an impact on synaptic strength and network functioning; these individual variations, in turn, have far-reaching effects on information processing within these networks over a lifetime.

Functional networks are differentially affected in various disease states. This has been intensively studied in the neurodegenerative diseases. There is mounting evidence, for example, that the pattern of disruption found in the coherence of various intrinsic brain networks is different for Alzheimer disease than for behavioral variant frontotemporal dementia.

Many psychiatric conditions have also been studied using functional connectivity approaches, including autism spectrum disease, PTSD, ADHD, bipolar disorder, and others.7 However, the heterogeneity of these disorders has made them difficult to study.

In MDD, there appear to be changes in connectivity within nodes of the anterior and posterior sub-networks of the DMN and altered connectivity between these areas and the SN as well as the CEN. The anterior sub-network of the DMN is believed to be involved in emotional and self-referential processing while the posterior sub-network of the DMN is more involved in memory and consciousness. Findings suggest that alterations in functional connectivity associated with MDD “. . . reflect a state of increased interaction between self-referential and emotional networks, and the dominance of negative self-referential over cognitive processing which corresponds to the clinical symptoms of depression.”13

Some of the potential applications for functional connectivity in clinical situations include as an aid in diagnosing, measuring disease severity, providing prognostic information, or monitoring disease progress and treatment effectiveness. For psychiatry, functional connectivity studies may help to differentiate subtypes within heterogeneous populations for disorders such as depression or schizophrenia.

Some neuropsychiatric “mysteries” are being solved through functional connectivity studies. For example, how do single lesions (as from a stroke) in different parts of the brain produce similar, complex neuropsychiatric syndromes in different individuals? Darby and colleagues14 were interested in this question as applied to the emergence of delusional misidentification syndromes after single lesions. Capgras syndrome is perhaps the best known of the delusional misidentification syndromes, exemplified by: “You look like my wife, but I know you are not my real wife; you are an imposter; my real wife is somewhere else.”

Darby found that the disparate lesions that had led to delusional misidentification were functionally connected to brain regions involved in familiarity assessment and in belief evaluation. This finding supports earlier theories about what goes wrong in delusional misidentification: first, the patient recognizes the individual or place but fails to experience that person or place as familiar; and second, there is a failure in “belief evaluation,” namely a failure to realize that it defies logic to believe that this is anything but the real person or place.

Extremely promising work by Emily S. Finn has shown that functional connectivity profiles are specific enough to distinguish individuals, including across different sessions in the scanner and across task and resting states.12 This identification of individuals is referred to as functional connectome fingerprinting. The ability to capture an individual’s uniqueness from data in the scanner is a truly remarkable achievement.

Conclusion

We have come a long way. It took centuries for the scientific world to understand the fundamentals of brain anatomy and neuro-cellular architecture. Even as progress was being made in mapping the brain’s neural circuitry, the goal of truly probing the complexity of human behavior felt like a distant star we would likely never reach. Yet, in the mere 25 years since MRI was first introduced into medicine, we have made astounding progress in probing the brain’s complexity. As psychiatrists, we feel a pressing need for new light to be shed on what goes wrong in mental disease. Powerful new scientific paradigms, advancing technology, and cross-discipline collaborations give us reason to be hopeful.

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