The idea of the default mode network, an interconnected set of brain regions that are active when the brain is resting and that power down during focused mental tasks, was first proposed in 2001, but it quickly became a hot topic in cognitive neuroscience and for Alzheimer disease researchers (see ARF related news story). The seesaw activation and deactivation of the default network and task-related brain regions appears critical for peak performance on memory tasks (see ARF related news story) and deactivation and network connectivity are disrupted early in Alzheimer disease, perhaps as a result of amyloid deposition (see Lustig et al., 2003 and ARF related news story).

AD is not the only disease where network activity suffers. A new study out in this week’s PNAS online edition reveals that function of the default network, as measured by fMRI, is also altered in people with schizophrenia, and their healthy first-degree relatives. The changes are somewhat different from those seen in AD—network connectivity is strengthened, for one, and overall network activity is increased both at rest and during task. The study, from Susan Whitfield-Gabrieli at MIT, suggests that changes in default mode activity and alterations in the normal balance between activation and deactivation contribute to the symptoms of schizophrenia, and could be part of the genetic risk for the disease.

To look at the default network in people with schizophrenia, the researchers performed fMRI scans on subjects while they were idle, and then when they engaged in a simple memory test. That allowed assessment of the basal network activity, and the extent of deactivation that occurred during a task that required concentration. The study compared 13 volunteers with early phase schizophrenia, 13 unaffected first-degree relatives and 13 healthy controls. When subjects performed the test, suppression of the default mode network was most effective in controls, and decreased in both patients and relatives. Overall, the deactivation in the default network and activation of task-related areas were strongly correlated in control subjects, but the seesaw effect was much weaker in patients and relatives. The result was a consistent hyperactivity of the default network. In addition, the patients and relatives showed stronger connectively between the medial prefrontal cortex and the precuneous and the rest of the default network, whether measured at rest or during a task.

The strength of connectivity and defect in deactivation correlated with poorer performance in the working memory task and stronger schizophrenia symptoms, suggesting that the default mode network could play an important role in the cognitive and clinical symptoms of schizophrenia. In addition, the observation that unaffected relatives show similar changes suggests that aberrant network activity stems from genetic risk and is causal, rather than just a consequence of the disease, Whitfield-Gabrieli told ARF. “In the future, it may be possible to use these fMRI measures as a way of diagnosing disease, or to figure out how patients are responding to treatment,” she said.

In terms of the symptoms of schizophrenia, it is interesting that the default mode network is activated during internal, self-referential thought. Network overactivity could lead to problems integrating the internal and external worlds, the authors speculate. Besides AD and schizophrenia, other diseases where default mode network activity is known to be altered include autism, epilepsy, depression, and attention deficit/hyperactivity disorder (for a comprehensive review of the literature of default mode and disease, see Broyd et al., 2008).

Connect the Neurons
Networks like the default mode involve widely distributed areas of the brain, but no matter how far-flung their components may be, the basic unit of any network comes down to neuron-to-neuron communication at individual synapses. A detailed map of the brain would greatly help researchers understand how default and other networks related to neurologic disease, and such a map requires an understanding of how individual neurons are wired to each other. However, it has been nearly impossible to tease out the exact contacts that make up even simple networks from the spaghetti-like tangle of axons, dendrites, and cell bodies that make up brain tissue. Now, three different groups have used sophisticated imaging techniques and single cell recording to get over that hurdle, and each has a paper in last week’s online edition of Nature revealing the connections that make up cortical neuron networks in mice.

First, Solange Brown and Shaul Hestrin of Stanford University looked at the local connections among three classes of cortical neurons that project over long distances to the contralateral cortex, the contralateral striatum, or the superior colliculus. After retrograde labeling of projections in live mice to distinguish the different kinds of neurons, the researchers simultaneously recorded activity in sets of four cells in cortical slices. By stimulating each cell in turn and watching the others’ responses, Brown and Hestrin showed they could identify local cortical connections with high success. The tendency of cells to make local connections was not random, or simply based on proximity, but depended on the identities of both the presynaptic and postsynaptic cells For example, a neuron that projected to the contralateral cortex (a corticocortical neuron) was four times more likely to make a synapse with a local corticotectal neuron than with another corticocortical neuron. The results suggest a way to unravel the local circuit architecture in the cortex.

In a second paper, researchers from Matthew Larkum’s lab at the University of Bern in Switzerland used fiber optic imaging to measure dendritic calcium changes and uncover a cortical inhibitory microcircuit in living rats. In the circuit, they found inhibitory interneurons govern the graded calcium response in L5 pyramidal neuron dendrites after sensory stimuli. The results help explain how neurons can respond to sensory input over a large dynamic range by utilizing cortical micronetworks.

Finally, Karel Svoboda and colleagues of the Howard Hughes Medical Institute in Ashburn, Virginia, traced out an excitatory circuit using photoactivation of neurons expressing channelrhodopsin (see Wang et al., 2007) to map out points of contact. Taking cortical slices with channelrhodopsin expressed in axons, first author Leopoldo Petreanu and colleagues used a laser beam to stimulate local neurotransmitter release while simultaneously recording from barrel cortex pyramidal neurons. If the axons made a synapse on the recorded cell, a postsynaptic excitatory current would be triggered. By directing channelrhodopsin expression to different layers of the cortex whose axons overlap with the pyramidal cell dendrites of the barrel cortex, the investigators were able to map input from the thalamus, cortical layers 4, 2/3, and part of the motor cortex to the dendrites of L3 and L5 pyramidal neurons. They found many of the known connections, as well as some new ones. In addition, the mapping revealed a high degree of spatial specificity, with different inputs mapping onto distal versus apical locations on the dendrites. By allowing the high-resolution look at neuronal circuits, the technique should help achieve a micro-scale, comprehensive picture of the hardware of the brain.—Pat McCaffrey


  1. This paper reminds me of our MCI and AD work with fMRI, where we also studied task-related suppression in the default mode network. We used an encoding task, but also an n-back working memory task (see Rombouts et al., 2005).

    Besides deactivation (or suppression during task), in this paper Whitfield-Gabrieli and colleagues also studied functional connectivity within the default mode network, and they find changes with schizophrenia. Further, they find an association of functional connectivity with psychopathology in patients.

    The analyses are extensive, combining deactivation and functional connectivity of the default mode network. This is a very interesting approach and the methodology could be useful for AD research. Also for AD, functional connectivity could be tested both with fMRI studies in which patients perform a task (task-fMRI), or are in resting state (no task-fMRI). The same could be applied to MCI patients and people at risk for AD.


    . Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: an fMRI study. Hum Brain Mapp. 2005 Dec;26(4):231-9. PubMed.

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News Citations

  1. Daydreams Traced to Default Network Activity
  2. Deactivation Flaws Predict Memory Troubles
  3. Tracing Alzheimer Disease Back to Source

Paper Citations

  1. . Functional deactivations: change with age and dementia of the Alzheimer type. Proc Natl Acad Sci U S A. 2003 Nov 25;100(24):14504-9. PubMed.
  2. . Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev. 2009 Mar;33(3):279-96. PubMed.

Further Reading

Primary Papers

  1. . Dendritic encoding of sensory stimuli controlled by deep cortical interneurons. Nature. 2009 Feb 26;457(7233):1137-41. PubMed.
  2. . Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A. 2009 Jan 27;106(4):1279-84. PubMed.
  3. . The subcellular organization of neocortical excitatory connections. Nature. 2009 Feb 26;457(7233):1142-5. PubMed.
  4. . Intracortical circuits of pyramidal neurons reflect their long-range axonal targets. Nature. 2009 Feb 26;457(7233):1133-6. PubMed.