Researchers begin to map the neurogenetics of neural network theory.
A new study from researchers at Stanford University found that synchronized physiological interactions between remote regional brain neuron networks have genetic underpinnings. The research was performed at Stanford but was made possible by collaborations with the Allen Institute for Brain Science and the IMAGEN Consortium, a multicenter European project. The study is published in the journal Science.
The team state that an emerging consensus among neuroscientists is that cognitive operations are performed not by individual brain regions working in isolation, but by networks consisting of several discrete brain regions, anatomically connected either directly via white-matter tracts or indirectly through intermediary nodes, that share ‘functional connectivity,’ meaning that activity in these regions is tightly coupled.
Previous studies show that any given functional network is normally most active during the performance of the task associated with that network, such as remembering dinner the night before and so on. However, the synchronous activity of component regions persists when networks are idling. Well over a dozen functional networks have been identified via a technique called resting-state functional magnetic resonance imaging.
In resting-state fMRI scans the researchers explain that the individual is asked to simply lie still and relax for several minutes. The results of these scans indicate that even at rest, the brain’s functional networks continue to hum along at their own distinguishable frequencies and phases, like different radio stations playing simultaneously, but quietly, on the same radio.
However, whether resting-state fMRI-derived images, which measure local blood flows in different places throughout the brain, actually reflect neuronal activity has been controversial. So the team wanted to dig deeper and get to the molecular underpinnings of these imaging results, which indicated that the brain maintains its exquisite functional-network architecture even at rest.
The current study computationally blended resting-state fMRI data obtained from eight-minute scans of 15 healthy adults whose sole instructions were to lie still and relax. This enabled the researchers to pinpoint numerous well-delineated functional networks. Hoping to find genes that might promote or at least be involved in functional connectivity, the team next sought gene-expression profiles, measurements of activity levels of each of the human genome’s approximately 20,000 known genes, of regions within corresponding functional networks.
Previous studies have shown that there’s no noninvasive way to obtain gene-profile expressions of brain tissue in living humans. However, the lab were able to use the massive amounts of carefully annotated and meticulously archived data derived from the Allen Institute’s collection of six post-mortem human brain samples. The institute’s scientists have obtained gene-expression profiles of several hundred tissue samples excised from specific locations throughout the brain.
The team then narrowed their focus to cortical areas associated with four functional networks that are all well characterized in the imaging literature. They all consist of discrete, noncontiguous regions in both hemispheres, and are well represented in the Allen Institute’s human-brain database. Along with the default-mode network associated with autobiographical memory, the current study looked at gene-expression profiles in component regions of the brain’s sensorimotor, visuospatial and salience (emotion) networks.
The researchers were hunting specifically for a set of genes whose expression rose or fell in a more synchronized fashion from region to region within a given network than between networks or outside any network. Using sophisticated statistical methods, they identified a set of 136 genes that showed a correlated pattern of gene expression in regions within each network.
The data findings showed that these 136 genes weren’t specific to any single network. Rather, any one of the genes that was being expressed at a high, intermediate or low level in one region of any network, regardless of which network picked, was also being expressed at corresponding levels in the other regions of that network. Importantly, the results showed that a number of these genes encode proteins that aid in nerve cells’ signature activity, propagating electrical impulses. Some are ion channels, which maintain and modulate voltage differences across nerve cells’ outer membranes. Others are found at the junctions where one nerve cell in a circuit contacts another.
The team validated their findings by turning to another database. The IMAGEN Consortium has conducted widespread imaging, cognitive and genetic tests on 14-year-olds in an effort to predict who’s at high risk of encountering problems such as substance abuse by age 16. Among other things, the IMAGEN database contains detailed information on tiny variations from the norm in subjects’ genomic sequences. The database notably spearheaded an analysis of the variants present in the 136 genes of interest in 259 healthy adolescents. These subjects’ network-connectivity strength was determined, in part, by the genetic-variant profiles of these 136 genes.
Additional experiments using tissue samples obtained from two additional data sets, the Allen Institute’s mouse-brain and mouse-connectivity atlases, confirmed and amplified the findings from research on human brains.
The researchers state that the identification of functional-connectivity-associated genes sets the stage for targeted clinical applications, such as finding out how neurodegeneration propagates within a network. They go on to add that their work holds potential implications for a number of neuropsychiatric disorders.
The team surmise that evidence suggests, for instance, that Alzheimer’s disease spreads from one brain region to the next within the brain’s so-called default-mode network, which is activated when a person is recalling recent autobiographical events. Resting-state imaging holds exceptional potential in cases where task-based fMRI isn’t applicable. Alzheimer’s patients, for example, have difficulty focusing on memory-based tasks.
Future work from the lab will focus on genes whose expression is correlated within one network, but not in other networks. The team conclude that focusing on default-mode network-specific genes, for example, may lend novel insights into Alzheimer’s disease.