Methods

Please find below a summary of experimental methods I use(d) for my research either directly or through collaborations with other researchers.

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(Functional) Magnetic Resonance Imaging ([f]MRI)

fMRI

(Functional) magnetic resonance imaging ([f]MRI) is used to visualize brain structure, as well as to indirectly measure brain activity through change in the magnetic properties of blood, and more precisely oxygenated versus deoxigenated hemoglobin.

During the resting state, oxygen concentration at a specific brain location is relatively low so that blood contains a high concentration of deoxy-hemoglobin. After neuronal activation, which leads to increased local oxygen consumption, more oxygen is trans-ported to the site of activation via heightened cerebral blood flow (CBF). This increased CBF entails a washout of deoxy-hemoglobin and an increased concentration of oxy-hemoglobin.

Importantly, deoxy-hemoglobin and oxy-hemoglobin have different magnetic properties – the former is paramagnetic while the latter is diamagnetic. Furthermore, the brain tissue that is surrounding blood vessels usually is diamagnetic. This means that during the resting state, there are more magnetic field inhomogeneities at the interfaces of vessels and brain tissue than after neuronal activation – or in other words, an increase in oxy-hemoglobin (and a concomitant decrease in deoxy-hemoglobin) makes the magnetic properties of blood and brain tissue more similar.

By detecting such changes in magnetic field inhomogeneities between blood vessels and adjacent brain tissue as a function of CBF and increased oxygen consumption, fMRI allows the detection of the so called blood-oxygen-level-dependent, or BOLD, signal. The higher the BOLD signal, the more a certain brain area is thought to have been activated by a certain experimental task. In comparison to other neuroimaging methods, fMRI offers a high spatial resolution and can measure brain activation in areas deep within the brain. In turn, fMRI has a relatively poor temporal resolution, because the BOLD signal unfolds within a time window of approximately 20 seconds.

See here for more details and further reading regarding fMRI.

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Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning

fnirs_2

fNIRS is similar to fMRI (see above) because it also measures blood oxygen levels – although it derives separate signals from oxy- and de-oxygenated blood. And there are some more differences.

Crucially, fNIRS detectors and emitters are placed on the scalp surface in a similar fashion to EEG electrodes (see below). Furthermore, fNRIS uses infrared light to measure changes in blood oxygenation, which is in contrast to the requirement of a strong magnetic field to measure BOLD with fMRI. Compared to EEG, fNIRS has a better spatial resolution. Compared to fMRI, fNIRS can only obtain measures from areas close to the scalp surface. The main advantage of fNIRS over both fMRI and EEG is its relatively weak susceptibility to movement artifacts. Moreover, fNIRS can be used in more naturalistic environments, particularly in two (or more) people directly interacting with each other (i.e. hyperscanning). We are therefore mainly employing fNIRS to look at inter-brain coherence in adult-adult as well as parent-child dyads within the greater context of investigating bio-behavioral synchrony (see also my recent blog here). 

See here for more details and further reading about fNIRS in general.

To establish a measure of inter-brain coherence relying upon the fNIRS signal from two participants assessed simultaneously, we rely upon the Wavelet Transform Coherence (WTC) method (Chang & Glover, 2010). An exemplary plot of computer-generated data can be found below (taken from here). 

wtc_phase

In the above image, two time-series (blue and red; bottom panel) are transformed by the continuous wavelet transform function, which decomposes a single time series into time-frequency space, into a time by frequency plot (top panel). The wavelet transform is a complex quantity whose modulus expresses the amount of power as a function of time and frequency (scale), and whose angle represents the local phase. By relating the wavelet transforms of two signals with each other, the cross-wavelet transform generates information on (i) the cross-wavelet power that expresses the amount of joint power as a function of time and frequency, and (ii) cross-wavelet phase that describes the relative phase of the two signals. Finally, the wavelet transform coherence (WTC) reveals localized regions of phase-locked behavior. It ranges from 0 and 1 (right scale, top panel), and can be conceptualized as a localized correlation coefficient in time and frequency space. 

Importantly, WTC is high if two signals are related to each other in time and frequency with any possible regularity. This means that the signals can be in phase (from 0 to 250), shifted in phase (from 250 to 700), or opposite in phase (from 800 to 1000). Conversely, WTC is low if two signals do not show any regularity in their time and frequency pattern  across a certain time window (from 700 to 800). For more information, also see here

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Electroencephalography (EEG)

This method measures electrical activity on the scalp surface.

 

EEG signal represents a more direct measure of brain activity, as it stems from ionic currents that flow within the nerve cells (neurons) – and not the indirect measure of brain activity relying on blood flow as used in fMRI and fNIRS. However, only the sum of synchronous activity of thousands or more neurons can be measured with EEG, because the electrical potentials of single neurons are too weak to be captured.

Usually, so called event-related potentials (ERPs) are derived from the EEG signal, which represent brain activity time-locked to the onset of a stimulus, i.e. an image or a sound.

While the spatial resolution or EEG is rather poor as compared to fMRI or fNIRS, it has a very high temporal resolution in the order of milliseconds.

See here for more details and further reading.

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Psychological Questionnaires & Personality Assessment

This method assesses individual differences and psychological traits of a person. Every person reacts differently to his/her environment or thoughts and emotions arising within the mind and body. Although fMRI, fNIRS, and EEG normally measure effects averaged over a group of participants, it is also of great interest to see how behavioral and/or brain activation patterns differ as a function of individual differences in personality traits.

One way to acquire variables reflecting individual differences in personality is to give participants a set of self-report questionnaires to fill in some time before or after measuring their behavior and/or brain activity. Another way, particularly in the context of my research on attachment, is to use age-appropriate semi-structured interviews such as the adult attachment interview (AAI) or the Story Stem Battery (SSB). These measures can furthermore be combined with behavioral coding of video-sequences, for example acquired during social interaction tasks. 

The psychological trait of main interest in my current research is attachment. Other measures include trait anxiety, behavioral inhibition vs. approach, positive and negative affect, internalization vs. externalization, empathy, resiliency, etc.

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Biological Markers of Well-Being and Health

crp

This method uses immunoassays to determine the concentration of different blood markers of well-being and health, including immune system function (i.e. IL-6 and CRP), and neural growth (i.e. BDNF). It also uses quantitative PCR (qPCR) to determine telomere lenght. Further markers are salivary and hair cortisol / cortisone.

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Genetics and Epigenetics

epigenetics-and-genetics2

Through blood or saliva samples, this method either establishes the participants’ genetic profile in terms of gene polymorphisms, or looks at epigenetic modification related to changes in gene function by means of methylation, particularly in the promoter region of genes of interest. Candidate genes include OXTR, NR3C1, DRD4, OPRM1, etc.

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 Linguistic Inquiry and Word Count (LIWC)

This method uses computerized text analysis to measure the relative frequency of words used in a written sample. For more information, see here.